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docs: Update READMEs with cloud version info and remove private repo references

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Robert Jakob
2025-05-28 13:52:15 +02:00
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14
Agent1_Peer_Review/README.md
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@@ -1,4 +1,8 @@
# AI Peer Reviewer
# Agent1_Peer_Review
> **Note:** This is an open-source project under the MIT License. We welcome contributions from the community to help improve the AI Peer Reviewer system. Please feel free to submit issues, pull requests, or suggestions for improvements.
> **Cloud Version Available:** A cloud version of the AI Peer Reviewer is now available at [https://www.rigorous.company/](https://www.rigorous.company/). Simply upload your manuscript, provide context on target journal and review focus, and receive a comprehensive PDF report via email within 1-2 working days. The cloud version is currently free for testing purposes.
A multi-agent system for comprehensive manuscript analysis and review.
@@ -198,13 +202,7 @@ MIT License
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
For detailed guidelines on how to contribute, please see [CONTRIBUTING.md](CONTRIBUTING.md).
This project is open source under the MIT License. We welcome contributions from the community to help improve the AI Peer Reviewer system. Please feel free to submit issues, pull requests, or suggestions for improvements.
## Join the Project

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Agent1_Peer_Review/context/context.json
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@@ -3,12 +3,12 @@
"label": "Target Publication Outlets (optional but recommended)",
"description": "This helps us tailor the review to your target venue's requirements.",
"placeholder": "e.g., Nature Medicine, Science, or specific conferences like NeurIPS 2024",
"user_input": "appetite journal"
"user_input": "Journal of Medical Internet Research (JMIR)"
},
"review_focus_areas": {
"label": "Review Focus Areas (optional but recommended)",
"description": "Specify any particular aspects you'd like the AI peer reviewers to focus on.",
"placeholder": "e.g., statistical analysis, methodology, experimental design, motivation, or specific aspects you want reviewers to focus on",
"user_input": "background literature and contribution introduction"
"user_input": "Introduction and discussion"
}
}

24
Agent2_Outlet_Fit/README.md
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@@ -1,3 +1,25 @@
# Agent2_Outlet_Fit
> **Note:** This module is currently in active development. It will help reviewers evaluate manuscripts against specific journal/conference criteria and support desk rejection decisions.
## Purpose
Agent2_Outlet_Fit is designed to:
- Evaluate manuscript fit with target journals/conferences
- Support journals/conferences in desk rejection decisions
- Enable researchers to pre-check manuscripts before submission
## Status
🚧 **In Development**
- Core functionality being implemented
- Integration with Agent1_Peer_Review in progress
- Testing and validation ongoing
## Contributing
This project is open source under the MIT License. We welcome contributions from the community to help improve the AI Peer Reviewer system. Please feel free to submit issues, pull requests, or suggestions for improvements.
## STATUS: 🚧 IN PLANNING PHASE
This tool is currently in the planning and development phase. It aims to serve two key purposes:
1. Help reviewers evaluate manuscripts against specific journal/conference criteria
@@ -78,7 +100,7 @@ A JSON report that includes:
Build a multiagent pipeline that automatically reverse-engineers a target outlet's expectations and assesses a manuscript's fit. The tool serves three key purposes:
1. **For Reviewers**: Streamline the review process by automatically checking manuscripts against journal/conference criteria
2. **For Journals/Conferences**: Support desk rejection decisions by providing automated preliminary screening
2. **For Journals/Conferences**: Support faster desk rejection decisions by providing automated preliminary screening and fast feedback to authors
3. **For Researchers**: Enable pre-submission self-assessment to identify potential issues before formal submission
This comprehensive approach aims to reduce desk rejection risk, improve submission strategy, and make the peer review process more efficient for all stakeholders.

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Backup/V2_Editorial_First_Decision_Support/.gitignore vendored
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# Environment variables
.env
# Manuscripts
manuscripts/
analysis_results/
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg
# Virtual Environment
venv/
ENV/
# IDE
.idea/
.vscode/
*.swp
*.swo
# OS
.DS_Store
Thumbs.db

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Backup/V2_Editorial_First_Decision_Support/README.md
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# V2 - Editorial First Decision Support
A tool that analyzes academic manuscripts against editorial requirements using OpenAI's GPT models.
## Features
- Extracts text from PDF manuscripts
- Analyzes manuscript against a list of editorial requirements
- Identifies which requirements are met and which are not
- Provides specific evidence for unmet requirements
- Generates a desk rejection recommendation
- Processes multiple PDFs in batch
## Setup
1. Clone this repository
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Set up your OpenAI API key:
- Either set it as an environment variable: `export OPENAI_API_KEY=your-key-here`
- Or provide it via command line argument: `--api-key your-key-here`
## Usage
1. Place your PDF manuscripts in the `manuscripts/` directory
2. Create a text file with your editorial requirements (one per line)
3. Run the checker:
```bash
python src/main.py --requirements path/to/requirements.txt
```
Optional arguments:
- `--manuscripts-dir`: Directory containing PDFs (default: manuscripts)
- `--output-dir`: Directory for analysis results (default: analysis_results)
- `--api-key`: Your OpenAI API key
## Project Structure
```
.
├── manuscripts/ # Directory for PDF manuscripts
├── analysis_results/ # Directory for analysis output files
├── src/ # Source code
│ ├── main.py
│ ├── pdf_parser.py
│ ├── openai_client.py
│ └── requirements_checker.py
├── requirements.txt # Python dependencies
└── example_requirements.txt # Example requirements file
```
## Example Requirements File
```
Manuscript must be under 5000 words
Abstract must be structured (Background, Methods, Results, Conclusion)
Figures must be in high resolution (300 DPI minimum)
```
## Output
For each PDF in the manuscripts directory, the tool will:
1. Create a separate analysis file in the `analysis_results/` directory
2. Name the file `{manuscript_name}_analysis.txt`
3. Include:
- Analysis of each requirement (met/not met)
- Evidence for unmet requirements
- Final desk rejection recommendation with justification
## Development
The project structure is modular and easy to extend:
- `pdf_parser.py`: Handles PDF text extraction
- `openai_client.py`: Manages OpenAI API interactions
- `requirements_checker.py`: Orchestrates the analysis process
- `main.py`: Provides the CLI interface

4
Backup/V2_Editorial_First_Decision_Support/requirements.txt
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openai>=1.0.0
PyMuPDF>=1.23.0
python-dotenv>=1.0.0
pytest>=7.0.0

15
Backup/V2_Editorial_First_Decision_Support/requirements_1.txt
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Manuscript must be under 5000 words
Abstract must be structured with Background, Methods, Results, and Conclusion sections
All figures must be in high resolution (600 DPI minimum) with detailed captions
Methods section must include comprehensive statistical analysis procedures
Results must be presented with appropriate statistical tests and p-values
References must follow APA format
All abbreviations must be defined at first use with a list of abbreviations provided
Conflict of interest statement must be included
Ethics approval must be mentioned if human subjects were involved
Data availability statement must be included
Funding sources must be acknowledged
Author contributions must be specified
Limitations of the study must be discussed
Future research directions must be suggested
Key findings must be summarized in a conclusion section

15
Backup/V2_Editorial_First_Decision_Support/requirements_2.txt
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Manuscript must be under 10000 words
Abstract should include key information about the study
Figures should be clear and readable
Methods section should describe the approach used
Results should be clearly presented
References should be consistent in format
Abbreviations should be explained where used
Conflict of interest statement is optional
Ethics approval should be mentioned if required by local regulations
Data availability statement is optional
Funding information can be included if relevant
Author contributions can be mentioned if desired
Study limitations can be discussed if relevant
Future work can be suggested if appropriate
A conclusion section is recommended

110
Backup/V2_Editorial_First_Decision_Support/src/main.py
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import argparse
import json
import os
from typing import List
from requirements_checker import RequirementsChecker
def read_requirements(requirements_path: str) -> List[str]:
"""
Read requirements from a text file.
Args:
requirements_path (str): Path to the requirements file
Returns:
List[str]: List of requirements
"""
with open(requirements_path, 'r') as f:
return [line.strip() for line in f if line.strip()]
def get_pdf_files(directory: str) -> List[str]:
"""
Get all PDF files from a directory.
Args:
directory (str): Path to the directory
Returns:
List[str]: List of PDF file paths
"""
pdf_files = []
for file in os.listdir(directory):
if file.lower().endswith('.pdf'):
pdf_files.append(os.path.join(directory, file))
return pdf_files
def analyze_manuscript(checker: RequirementsChecker, pdf_path: str, requirements: List[str], output_dir: str) -> None:
"""
Analyze a single manuscript and save results to a file.
Args:
checker (RequirementsChecker): The requirements checker instance
pdf_path (str): Path to the PDF file
requirements (List[str]): List of requirements to check
output_dir (str): Directory to save the results
"""
try:
# Get the base filename without extension
base_name = os.path.splitext(os.path.basename(pdf_path))[0]
# Analyze manuscript
results = checker.check_manuscript(pdf_path, requirements)
# Format results
formatted_results = checker.format_results(results)
# Save results to file
output_file = os.path.join(output_dir, f"{base_name}_analysis.txt")
with open(output_file, 'w') as f:
f.write(formatted_results)
print(f"Analysis completed for {base_name}")
print(f"Results saved to: {output_file}\n")
except Exception as e:
print(f"Error processing {pdf_path}: {str(e)}\n")
def main():
parser = argparse.ArgumentParser(description='Manuscript Requirements Checker')
parser.add_argument('--manuscripts-dir', default='manuscripts',
help='Directory containing PDF manuscripts (default: manuscripts)')
parser.add_argument('--requirements', required=True, help='Path to the requirements text file')
parser.add_argument('--output-dir', default='analysis_results',
help='Directory to save analysis results (default: analysis_results)')
parser.add_argument('--api-key', help='OpenAI API key (optional if set in environment)')
args = parser.parse_args()
try:
# Create output directory if it doesn't exist
os.makedirs(args.output_dir, exist_ok=True)
# Read requirements
requirements = read_requirements(args.requirements)
# Get PDF files
pdf_files = get_pdf_files(args.manuscripts_dir)
if not pdf_files:
print(f"No PDF files found in {args.manuscripts_dir}")
return 1
print(f"Found {len(pdf_files)} PDF files to analyze")
# Initialize checker
checker = RequirementsChecker(api_key=args.api_key)
# Process each PDF
for pdf_path in pdf_files:
analyze_manuscript(checker, pdf_path, requirements, args.output_dir)
print("Analysis complete!")
except Exception as e:
print(f"Error: {str(e)}")
return 1
return 0
if __name__ == '__main__':
exit(main())

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Backup/V2_Editorial_First_Decision_Support/src/openai_client.py
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@@ -1,134 +0,0 @@
import os
import json
from typing import List, Dict, Any
from openai import OpenAI
from dotenv import load_dotenv
class OpenAIClient:
"""A class to handle interactions with the OpenAI API."""
def __init__(self, api_key: str = None):
"""
Initialize the OpenAI client.
Args:
api_key (str, optional): OpenAI API key. If not provided, will try to load from environment.
"""
# Try to load .env from the current directory
load_dotenv()
# If API key is not found, try to load from parent directory
if not os.getenv("OPENAI_API_KEY"):
# Get the path to the parent directory (two levels up from this file)
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
env_path = os.path.join(parent_dir, ".env")
if os.path.exists(env_path):
load_dotenv(env_path)
self.api_key = api_key or os.getenv("OPENAI_API_KEY")
if not self.api_key:
raise ValueError("OpenAI API key is required")
self.client = OpenAI(api_key=self.api_key)
def check_requirements(self, manuscript_text: str, requirements: List[str]) -> Dict[str, Any]:
"""
Check if the manuscript meets the given requirements using GPT-3.5-turbo.
Args:
manuscript_text (str): The full text of the manuscript
requirements (List[str]): List of editorial requirements to check
Returns:
Dict[str, Any]: Analysis results including requirement status and evidence
"""
# Truncate manuscript text to first 4000 words to reduce token usage
words = manuscript_text.split()
truncated_text = ' '.join(words[:4000]) if len(words) > 4000 else manuscript_text
prompt = self._create_analysis_prompt(truncated_text, requirements)
try:
response = self.client.chat.completions.create(
model="gpt-3.5-turbo", # Using standard model instead of 16k for cost efficiency
messages=[
{"role": "system", "content": "You are an expert manuscript reviewer. Analyze manuscripts against requirements. Be strict and thorough. Only mark requirements as met with clear evidence. Provide specific quotes and exact numbers when applicable. Always respond with valid JSON."},
{"role": "user", "content": prompt}
],
temperature=0.3,
max_tokens=1000 # Limit response length
)
response_content = response.choices[0].message.content
print(f"OpenAI Response: {response_content}") # Debug print
return self._parse_response(response_content)
except Exception as e:
raise Exception(f"Failed to analyze manuscript: {str(e)}")
def _create_analysis_prompt(self, manuscript_text: str, requirements: List[str]) -> str:
"""
Create a prompt for the requirements analysis.
Args:
manuscript_text (str): The manuscript text
requirements (List[str]): List of requirements to check
Returns:
str: Formatted prompt for the OpenAI API
"""
requirements_section = "\n".join([f"{i+1}. {req}" for i, req in enumerate(requirements)])
return f"""Please analyze the following manuscript against these requirements:
{requirements_section}
For each requirement:
1. Determine if it is met (YES/NO)
2. Provide evidence from the text
3. Give a brief explanation
Manuscript text:
{manuscript_text}
Please format your response as a JSON object with the following structure:
{{
"requirements_analysis": [
{{
"requirement": "<requirement text>",
"is_met": <true/false>,
"evidence": "<specific evidence from the text>",
"explanation": "<brief explanation>"
}}
],
"desk_rejection_recommendation": {{
"should_reject": <true/false>,
"justification": "<detailed explanation of the recommendation>"
}}
}}"""
def _parse_response(self, response: str) -> Dict[str, Any]:
"""
Parse the OpenAI API response into a structured format.
Args:
response (str): Raw response from the API
Returns:
Dict[str, Any]: Parsed response
"""
try:
# Remove code block markers if present
cleaned_response = response.strip()
if cleaned_response.startswith("```"):
cleaned_response = cleaned_response.split("\n", 1)[1] # Remove first line
if cleaned_response.endswith("```"):
cleaned_response = cleaned_response.rsplit("\n", 1)[0] # Remove last line
if cleaned_response.startswith("json"):
cleaned_response = cleaned_response.split("\n", 1)[1] # Remove "json" line
return json.loads(cleaned_response)
except json.JSONDecodeError as e:
print(f"Failed to parse JSON: {str(e)}") # Debug print
print(f"Response content: {response}") # Debug print
raise Exception("Failed to parse OpenAI response as JSON")

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Backup/V2_Editorial_First_Decision_Support/src/pdf_parser.py
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import fitz # PyMuPDF
import re
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
@dataclass
class TextBlock:
"""Represents a block of text with its properties."""
text: str
page: int
font_size: float
font_name: str
is_bold: bool
is_italic: bool
bbox: Tuple[float, float, float, float]
class PDFParser:
"""A class to parse PDF manuscripts with advanced text extraction capabilities."""
def __init__(self, pdf_path: str):
"""
Initialize the PDF parser.
Args:
pdf_path (str): Path to the PDF file
"""
self.pdf_path = pdf_path
self.doc = None
self.text_blocks = []
def extract_text(self) -> str:
"""
Extract text from the PDF with structure preservation.
Returns:
str: Extracted and structured text
"""
try:
self.doc = fitz.open(self.pdf_path)
self.text_blocks = []
# Process first 10 pages or less
max_pages = min(10, len(self.doc))
for page_num in range(max_pages):
page = self.doc[page_num]
blocks = self._extract_page_blocks(page, page_num)
self.text_blocks.extend(blocks)
# Sort blocks by position and process
self.text_blocks.sort(key=lambda b: (b.page, b.bbox[1], b.bbox[0]))
# Combine blocks into structured text
structured_text = self._combine_blocks()
return structured_text
except Exception as e:
raise Exception(f"Failed to extract text from PDF: {str(e)}")
finally:
if self.doc:
self.doc.close()
def _extract_page_blocks(self, page: fitz.Page, page_num: int) -> List[TextBlock]:
"""
Extract text blocks from a page with formatting information.
Args:
page (fitz.Page): PDF page
page_num (int): Page number
Returns:
List[TextBlock]: List of text blocks with formatting
"""
blocks = []
# Get text with formatting information
text_dict = page.get_text("dict")
for block in text_dict.get("blocks", []):
for line in block.get("lines", []):
for span in line.get("spans", []):
# Extract text and formatting
text = span.get("text", "").strip()
if not text:
continue
font = span.get("font", "")
size = span.get("size", 0)
bbox = span.get("bbox", [0, 0, 0, 0])
# Check for bold/italic
flags = span.get("flags", 0)
is_bold = bool(flags & 2**1) # Check bold flag
is_italic = bool(flags & 2**0) # Check italic flag
blocks.append(TextBlock(
text=text,
page=page_num + 1,
font_size=size,
font_name=font,
is_bold=is_bold,
is_italic=is_italic,
bbox=tuple(bbox)
))
return blocks
def _combine_blocks(self) -> str:
"""
Combine text blocks into structured text.
Returns:
str: Structured text
"""
structured_text = []
current_section = None
for block in self.text_blocks:
text = block.text
# Detect headers based on font size and style
if block.font_size > 12 and block.is_bold:
if current_section:
structured_text.append("\n")
current_section = text
structured_text.append(f"\n{text}\n")
else:
# Regular text
structured_text.append(text)
# Add space between paragraphs
if text.endswith(('.', '!', '?')):
structured_text.append("\n")
return " ".join(structured_text)
def get_word_count(self, text: str) -> int:
"""
Get the word count of the text.
Args:
text (str): Text to count words in
Returns:
int: Word count
"""
return len(text.split())
def detect_sections(self) -> Dict[str, List[str]]:
"""
Detect major sections in the document.
Returns:
Dict[str, List[str]]: Dictionary of sections and their content
"""
sections = {}
current_section = "Introduction"
current_content = []
for block in self.text_blocks:
# Detect section headers
if block.font_size > 12 and block.is_bold:
if current_content:
sections[current_section] = current_content
current_section = block.text
current_content = []
else:
current_content.append(block.text)
# Add the last section
if current_content:
sections[current_section] = current_content
return sections

80
Backup/V2_Editorial_First_Decision_Support/src/requirements_checker.py
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@@ -1,80 +0,0 @@
from typing import List, Dict, Any
from pdf_parser import PDFParser
from openai_client import OpenAIClient
class RequirementsChecker:
"""A class to check manuscript requirements using OpenAI's GPT model."""
def __init__(self, api_key: str = None):
"""
Initialize the requirements checker.
Args:
api_key (str, optional): OpenAI API key
"""
self.openai_client = OpenAIClient(api_key)
def check_manuscript(self, pdf_path: str, requirements: List[str]) -> Dict[str, Any]:
"""
Check if a manuscript meets the given requirements.
Args:
pdf_path (str): Path to the PDF manuscript
requirements (List[str]): List of requirements to check
Returns:
Dict[str, Any]: Analysis results
"""
# Parse PDF with structure preservation
pdf_parser = PDFParser(pdf_path)
manuscript_text = pdf_parser.extract_text()
# Get sections for better context
sections = pdf_parser.detect_sections()
# Calculate word count
word_count = len(manuscript_text.split())
# Add metadata and section information to the text
structured_text = f"""Document Metadata:
Word Count: {word_count} words
Document Structure:
"""
for section, content in sections.items():
section_text = ' '.join(content)
section_word_count = len(section_text.split())
structured_text += f"\n{section} ({section_word_count} words):\n{section_text}\n"
# Check requirements using OpenAI
analysis = self.openai_client.check_requirements(structured_text, requirements)
return analysis
def format_results(self, results: Dict[str, Any]) -> str:
"""
Format the analysis results into a readable string.
Args:
results (Dict[str, Any]): Analysis results from OpenAI
Returns:
str: Formatted results
"""
output = []
output.append("=== Manuscript Requirements Analysis ===\n")
# Format requirements analysis
for req_analysis in results["requirements_analysis"]:
output.append(f"Requirement: {req_analysis['requirement']}")
output.append(f"Status: {'✓ Met' if req_analysis['is_met'] else '✗ Not Met'}")
output.append(f"Evidence: {req_analysis['evidence']}")
output.append(f"Explanation: {req_analysis['explanation']}\n")
# Format desk rejection recommendation
rejection = results["desk_rejection_recommendation"]
output.append("=== Final Recommendation ===")
output.append(f"Desk Rejection: {'Yes' if rejection['should_reject'] else 'No'}")
output.append(f"Justification: {rejection['justification']}")
return "\n".join(output)

38
Backup/V2_Editorial_First_Decision_Support/test_api.py
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@@ -1,38 +0,0 @@
import os
from dotenv import load_dotenv
from openai import OpenAI
def test_api_key():
# Force reload of environment variables
load_dotenv(override=True)
# Get API key
api_key = os.getenv("OPENAI_API_KEY")
print(f"API Key loaded: {'Yes' if api_key else 'No'}")
if api_key:
print(f"API Key starts with: {api_key[:7]}...")
print(f"API Key length: {len(api_key)}")
# Print current working directory and .env file location
print(f"\nCurrent working directory: {os.getcwd()}")
env_path = os.path.join(os.getcwd(), '.env')
print(f"Looking for .env file at: {env_path}")
print(f".env file exists: {os.path.exists(env_path)}")
try:
# Initialize OpenAI client
client = OpenAI(api_key=api_key)
# Make a simple API call
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello!"}],
max_tokens=5
)
print("\nAPI call successful!")
print(f"Response: {response.choices[0].message.content}")
except Exception as e:
print(f"\nError: {str(e)}")
if __name__ == "__main__":
test_api_key()

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Backup/V3_Peer_Review/README.md
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@@ -1,81 +0,0 @@
# Academic Manuscript Peer Review Tool
This tool uses OpenAI's GPT-4 to perform automated peer reviews of academic manuscripts. It analyzes PDF manuscripts against a set of review criteria and provides detailed feedback, scores, and recommendations.
## Features
- Automated peer review of academic manuscripts
- Comprehensive analysis across multiple review criteria
- Detailed feedback with specific examples and suggestions
- Metadata extraction and document structure analysis
- Support for multiple PDF files
- Configurable review criteria
## Installation
1. Clone the repository
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Create a `.env` file in the root directory with your OpenAI API key:
```
OPENAI_API_KEY=your_api_key_here
```
Note: The tool will look for the .env file in the current directory first, then in the parent directory.
## Usage
1. Place your PDF manuscripts in the `manuscripts` directory
2. (Optional) Customize the review criteria in `review_criteria.json`
3. Run the review tool:
```bash
python src/main.py --criteria review_criteria.json
```
### Command Line Arguments
- `--manuscripts-dir`: Directory containing PDF manuscripts (default: `manuscripts`)
- `--criteria`: Path to the review criteria JSON file (required)
- `--output-dir`: Directory to save review results (default: `analysis_results`)
- `--api-key`: OpenAI API key (optional if set in environment)
## Review Criteria
The tool evaluates manuscripts against the following criteria:
1. Originality and Innovation
2. Methodology
3. Results and Analysis
4. Writing and Presentation
5. Technical Accuracy
6. Literature Review
7. Figures and Tables
8. References
9. Ethical Considerations
10. Impact and Significance
Each criterion is scored on a scale of 1-5, with detailed feedback and specific examples provided.
## Output Format
The review results are saved in text files with the following sections:
- Manuscript Metadata
- Document Statistics
- Overall Assessment
- Detailed Assessment (per criterion)
- Score
- Feedback
- Examples
- Suggestions for Improvement
## Requirements
- Python 3.7+
- OpenAI API key
- PDF manuscripts to review
## License
This project is licensed under the MIT License - see the LICENSE file for details.

3
Backup/V3_Peer_Review/requirements.txt
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@@ -1,3 +0,0 @@
openai>=1.0.0
python-dotenv>=0.19.0
PyPDF2>=3.0.0

12
Backup/V3_Peer_Review/review_criteria.json
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@@ -1,12 +0,0 @@
{
"Originality and Innovation": "Assess the novelty and originality of the research. Consider if the work makes a significant contribution to the field and introduces new ideas or approaches.",
"Methodology": "Evaluate the research design, methods, and procedures. Consider if they are appropriate, well-described, and rigorously implemented.",
"Results and Analysis": "Assess the presentation and analysis of results. Consider if the data is properly analyzed, interpreted, and presented in a clear and logical manner.",
"Writing and Presentation": "Evaluate the clarity, organization, and quality of the writing. Consider if the manuscript is well-structured, easy to follow, and free of major language issues.",
"Technical Accuracy": "Assess the technical accuracy of the content, including mathematical derivations, statistical analyses, and experimental procedures.",
"Literature Review": "Evaluate the comprehensiveness and relevance of the literature review. Consider if it adequately covers the field and provides proper context for the research.",
"Figures and Tables": "Assess the quality and appropriateness of figures and tables. Consider if they are clear, well-labeled, and effectively support the text.",
"References": "Evaluate the completeness and accuracy of references. Consider if they are properly formatted and relevant to the research.",
"Ethical Considerations": "Assess if the research adheres to ethical standards and guidelines. Consider issues such as informed consent, data privacy, and conflict of interest.",
"Impact and Significance": "Evaluate the potential impact and significance of the research. Consider if it addresses an important question and has the potential to influence the field."
}

110
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import argparse
import json
import os
from typing import Dict, List
from peer_review_checker import PeerReviewChecker
def read_review_criteria(criteria_path: str) -> Dict[str, str]:
"""
Read review criteria from a JSON file.
Args:
criteria_path (str): Path to the criteria file
Returns:
Dict[str, str]: Dictionary of criteria and their descriptions
"""
with open(criteria_path, 'r') as f:
return json.load(f)
def get_pdf_files(directory: str) -> List[str]:
"""
Get all PDF files from a directory.
Args:
directory (str): Path to the directory
Returns:
List[str]: List of PDF file paths
"""
pdf_files = []
for file in os.listdir(directory):
if file.lower().endswith('.pdf'):
pdf_files.append(os.path.join(directory, file))
return pdf_files
def review_manuscript(checker: PeerReviewChecker, pdf_path: str, criteria: Dict[str, str], output_dir: str) -> None:
"""
Review a single manuscript and save results to a file.
Args:
checker (PeerReviewChecker): The peer review checker instance
pdf_path (str): Path to the PDF file
criteria (Dict[str, str]): Review criteria
output_dir (str): Directory to save the results
"""
try:
# Get the base filename without extension
base_name = os.path.splitext(os.path.basename(pdf_path))[0]
# Review manuscript
results = checker.review_manuscript(pdf_path, criteria)
# Format results
formatted_results = checker.format_results(results)
# Save results to file
output_file = os.path.join(output_dir, f"{base_name}_review.txt")
with open(output_file, 'w') as f:
f.write(formatted_results)
print(f"Review completed for {base_name}")
print(f"Results saved to: {output_file}\n")
except Exception as e:
print(f"Error processing {pdf_path}: {str(e)}\n")
def main():
parser = argparse.ArgumentParser(description='Academic Manuscript Peer Review Tool')
parser.add_argument('--manuscripts-dir', default='manuscripts',
help='Directory containing PDF manuscripts (default: manuscripts)')
parser.add_argument('--criteria', required=True, help='Path to the review criteria JSON file')
parser.add_argument('--output-dir', default='analysis_results',
help='Directory to save review results (default: analysis_results)')
parser.add_argument('--api-key', help='OpenAI API key (optional if set in environment)')
args = parser.parse_args()
try:
# Create output directory if it doesn't exist
os.makedirs(args.output_dir, exist_ok=True)
# Read review criteria
criteria = read_review_criteria(args.criteria)
# Get PDF files
pdf_files = get_pdf_files(args.manuscripts_dir)
if not pdf_files:
print(f"No PDF files found in {args.manuscripts_dir}")
return 1
print(f"Found {len(pdf_files)} PDF files to review")
# Initialize checker
checker = PeerReviewChecker(api_key=args.api_key)
# Process each PDF
for pdf_path in pdf_files:
review_manuscript(checker, pdf_path, criteria, args.output_dir)
print("Review process complete!")
except Exception as e:
print(f"Error: {str(e)}")
return 1
return 0
if __name__ == '__main__':
exit(main())

124
Backup/V3_Peer_Review/src/openai_client.py
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import os
import json
from typing import List, Dict, Any
from openai import OpenAI
from dotenv import load_dotenv
class OpenAIClient:
"""A class to handle interactions with the OpenAI API for peer review."""
def __init__(self, api_key: str = None):
"""
Initialize the OpenAI client.
Args:
api_key (str, optional): OpenAI API key. If not provided, will try to load from environment.
"""
# Try to load .env from the current directory
load_dotenv()
# If API key is not found, try to load from parent directory
if not os.getenv("OPENAI_API_KEY"):
# Get the path to the parent directory (two levels up from this file)
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
env_path = os.path.join(parent_dir, ".env")
if os.path.exists(env_path):
load_dotenv(env_path)
self.api_key = api_key or os.getenv("OPENAI_API_KEY")
if not self.api_key:
raise ValueError("OpenAI API key is required")
self.client = OpenAI(api_key=self.api_key)
def analyze_manuscript(self, manuscript_text: str, review_criteria: Dict[str, str]) -> Dict[str, Any]:
"""
Analyze a manuscript using GPT-4 for comprehensive peer review.
Args:
manuscript_text (str): The full text of the manuscript
review_criteria (Dict[str, str]): Dictionary of review criteria and their descriptions
Returns:
Dict[str, Any]: Analysis results including scores and detailed feedback
"""
# Truncate manuscript text to first 4000 words to reduce token usage
words = manuscript_text.split()
truncated_text = ' '.join(words[:4000]) if len(words) > 4000 else manuscript_text
prompt = self._create_review_prompt(truncated_text, review_criteria)
try:
response = self.client.chat.completions.create(
model="gpt-4", # Using GPT-4 for more sophisticated analysis
messages=[
{"role": "system", "content": "You are an expert peer reviewer with extensive experience in academic publishing. Analyze manuscripts thoroughly and provide detailed, constructive feedback. Be objective and evidence-based in your assessment."},
{"role": "user", "content": prompt}
],
temperature=0.3,
max_tokens=2000 # Increased token limit for detailed feedback
)
return self._parse_response(response.choices[0].message.content)
except Exception as e:
raise Exception(f"Failed to analyze manuscript: {str(e)}")
def _create_review_prompt(self, manuscript_text: str, review_criteria: Dict[str, str]) -> str:
"""
Create a prompt for the peer review analysis.
Args:
manuscript_text (str): The manuscript text
review_criteria (Dict[str, str]): Review criteria and descriptions
Returns:
str: Formatted prompt for the OpenAI API
"""
criteria_section = "\n".join([f"- {criterion}: {description}"
for criterion, description in review_criteria.items()])
return f"""Please analyze the following manuscript according to these criteria:
{criteria_section}
For each criterion:
1. Provide a score from 1-5 (1 being lowest, 5 being highest)
2. Give detailed, constructive feedback
3. Support your assessment with specific examples from the text
4. Suggest specific improvements where applicable
Manuscript text:
{manuscript_text}
Please format your response as a JSON object with the following structure:
{{
"overall_assessment": {{
"score": <1-5>,
"summary": "<brief summary of overall assessment>"
}},
"criteria_assessments": {{
"<criterion_name>": {{
"score": <1-5>,
"feedback": "<detailed feedback>",
"examples": ["<specific example 1>", "<specific example 2>"],
"suggestions": ["<improvement suggestion 1>", "<improvement suggestion 2>"]
}}
}},
"recommendation": "<accept/revise/reject>",
"confidence": <0-1>
}}"""
def _parse_response(self, response: str) -> Dict[str, Any]:
"""
Parse the OpenAI API response into a structured format.
Args:
response (str): Raw response from the API
Returns:
Dict[str, Any]: Parsed response
"""
try:
return json.loads(response)
except json.JSONDecodeError:
raise Exception("Failed to parse OpenAI response as JSON")

117
Backup/V3_Peer_Review/src/pdf_parser.py
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import os
import re
from typing import Dict, List, Tuple
import PyPDF2
class PDFParser:
"""A class to parse PDF manuscripts and extract structured content."""
def __init__(self, pdf_path: str):
"""
Initialize the PDF parser.
Args:
pdf_path (str): Path to the PDF file
"""
if not os.path.exists(pdf_path):
raise FileNotFoundError(f"PDF file not found: {pdf_path}")
self.pdf_path = pdf_path
def extract_text(self) -> str:
"""
Extract text from the PDF file.
Returns:
str: Extracted text
"""
try:
with open(self.pdf_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
text = ""
for page in reader.pages:
text += page.extract_text() + "\n"
return text.strip()
except Exception as e:
raise Exception(f"Failed to extract text from PDF: {str(e)}")
def detect_sections(self) -> Dict[str, List[str]]:
"""
Detect and extract sections from the manuscript.
Returns:
Dict[str, List[str]]: Dictionary of section names and their content
"""
text = self.extract_text()
# Common section headers in academic papers
section_patterns = {
'Abstract': r'Abstract[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)',
'Introduction': r'Introduction[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)',
'Methods': r'(Methods|Methodology|Materials and Methods)[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)',
'Results': r'Results[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)',
'Discussion': r'Discussion[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)',
'Conclusion': r'(Conclusion|Conclusions)[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)',
'References': r'(References|Bibliography)[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:|$)'
}
sections = {}
for section_name, pattern in section_patterns.items():
matches = re.finditer(pattern, text, re.IGNORECASE)
for match in matches:
section_text = match.group(0).strip()
# Clean up the section text
section_text = re.sub(r'^\w+\s*', '', section_text) # Remove section header
sections[section_name] = section_text.split('\n')
return sections
def get_metadata(self) -> Dict[str, str]:
"""
Extract metadata from the PDF.
Returns:
Dict[str, str]: Dictionary of metadata
"""
try:
with open(self.pdf_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
metadata = reader.metadata
return {
'title': metadata.get('/Title', 'Unknown'),
'author': metadata.get('/Author', 'Unknown'),
'creation_date': metadata.get('/CreationDate', 'Unknown'),
'page_count': str(len(reader.pages))
}
except Exception as e:
raise Exception(f"Failed to extract metadata from PDF: {str(e)}")
def get_references(self) -> List[str]:
"""
Extract references from the manuscript.
Returns:
List[str]: List of references
"""
sections = self.detect_sections()
if 'References' in sections:
return sections['References']
return []
def get_figures_and_tables(self) -> Tuple[List[str], List[str]]:
"""
Extract figures and tables from the manuscript.
Returns:
Tuple[List[str], List[str]]: Lists of figures and tables
"""
text = self.extract_text()
# Simple pattern matching for figures and tables
figure_pattern = r'Figure \d+[.:].*?(?=\n\n|\n[A-Z][a-z]+:)'
table_pattern = r'Table \d+[.:].*?(?=\n\n|\n[A-Z][a-z]+:)'
figures = re.findall(figure_pattern, text, re.IGNORECASE | re.DOTALL)
tables = re.findall(table_pattern, text, re.IGNORECASE | re.DOTALL)
return figures, tables

121
Backup/V3_Peer_Review/src/peer_review_checker.py
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from typing import Dict, Any, List
from pdf_parser import PDFParser
from openai_client import OpenAIClient
class PeerReviewChecker:
"""A class to coordinate the peer review process."""
def __init__(self, api_key: str = None):
"""
Initialize the peer review checker.
Args:
api_key (str, optional): OpenAI API key
"""
self.openai_client = OpenAIClient(api_key)
def review_manuscript(self, pdf_path: str, review_criteria: Dict[str, str]) -> Dict[str, Any]:
"""
Review a manuscript using the specified criteria.
Args:
pdf_path (str): Path to the PDF manuscript
review_criteria (Dict[str, str]): Dictionary of review criteria and their descriptions
Returns:
Dict[str, Any]: Review results
"""
# Parse PDF
pdf_parser = PDFParser(pdf_path)
# Get manuscript metadata
metadata = pdf_parser.get_metadata()
# Extract text and structure
manuscript_text = pdf_parser.extract_text()
sections = pdf_parser.detect_sections()
# Get references and figures/tables
references = pdf_parser.get_references()
figures, tables = pdf_parser.get_figures_and_tables()
# Add metadata and structure information to the text
structured_text = f"""Document Metadata:
Title: {metadata['title']}
Author: {metadata['author']}
Pages: {metadata['page_count']}
Creation Date: {metadata['creation_date']}
Document Structure:
"""
for section, content in sections.items():
section_text = ' '.join(content)
section_word_count = len(section_text.split())
structured_text += f"\n{section} ({section_word_count} words):\n{section_text}\n"
# Add references and figures/tables information
structured_text += f"\nReferences ({len(references)}):\n" + "\n".join(references)
structured_text += f"\n\nFigures ({len(figures)}):\n" + "\n".join(figures)
structured_text += f"\n\nTables ({len(tables)}):\n" + "\n".join(tables)
# Analyze manuscript using OpenAI
analysis = self.openai_client.analyze_manuscript(structured_text, review_criteria)
# Add metadata to the analysis results
analysis['metadata'] = metadata
analysis['statistics'] = {
'total_references': len(references),
'total_figures': len(figures),
'total_tables': len(tables),
'total_sections': len(sections)
}
return analysis
def format_results(self, results: Dict[str, Any]) -> str:
"""
Format the review results into a readable string.
Args:
results (Dict[str, Any]): Review results
Returns:
str: Formatted results
"""
output = []
# Add metadata section
output.append("=== Manuscript Metadata ===")
for key, value in results['metadata'].items():
output.append(f"{key}: {value}")
# Add statistics section
output.append("\n=== Document Statistics ===")
for key, value in results['statistics'].items():
output.append(f"{key}: {value}")
# Add overall assessment
output.append("\n=== Overall Assessment ===")
output.append(f"Score: {results['overall_assessment']['score']}/5")
output.append(f"Summary: {results['overall_assessment']['summary']}")
output.append(f"Recommendation: {results['recommendation']}")
output.append(f"Confidence: {results['confidence']*100:.1f}%")
# Add criteria assessments
output.append("\n=== Detailed Assessment ===")
for criterion, assessment in results['criteria_assessments'].items():
output.append(f"\n{criterion}")
output.append(f"Score: {assessment['score']}/5")
output.append(f"Feedback: {assessment['feedback']}")
if assessment['examples']:
output.append("\nExamples:")
for example in assessment['examples']:
output.append(f"- {example}")
if assessment['suggestions']:
output.append("\nSuggestions for Improvement:")
for suggestion in assessment['suggestions']:
output.append(f"- {suggestion}")
return "\n".join(output)

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Backup/V4_multi_agent/README.md
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# Multi-Agent Scientific Paper Review System
A sophisticated system that uses multiple AI agents to provide comprehensive peer review of scientific papers. The system simulates a team of expert reviewers, each with specific expertise and focus areas, to analyze papers from multiple perspectives.
## Features
### 1. Specialized Review Agents
- **Core Reviewers** (Always included):
- Language and Style Expert
- Methodology Expert
- Ethics and Compliance Expert
- Literature Review Expert
- Impact and Significance Expert
- Research Gap and Contribution Expert
- **Domain-Specific Reviewers** (Added based on paper content):
- Technical Area Experts (e.g., Machine Learning, Healthcare Systems)
- Field-Specific Experts (e.g., Computer Science, Medical Research)
- **Specialized Reviewers** (Added based on requirements):
- Data Analysis Expert
- Experimental Design Expert
- Literature Coverage Expert
- Research Gap and Contribution Expert
### 2. Key Scientist Simulation
- Identifies influential scientists in the field
- Simulates their likely perspective and feedback
- Considers their research focus and typical review style
- Provides realistic reviewer comments
### 3. Comprehensive Analysis
- Paper analysis and domain identification
- Research gap analysis
- Contribution positioning
- Literature coverage assessment
- Methodology evaluation
- Impact assessment
### 4. Detailed Reporting
- Executive summary
- Individual reviewer feedback
- Thematic analysis across reviews
- Specific recommendations
- Final assessment and timeline
## Technical Details
### Model Configuration
- Supports multiple OpenAI models:
- Default: GPT-3.5-turbo (for testing)
- Production: GPT-4-turbo-preview (for high-quality reviews)
- Easy model switching for different use cases
### Output Format
```json
{
"review_plan": {
"paper_analysis": {...},
"review_team": {...},
"key_scientists": [...],
"simulated_feedback": {...}
},
"final_report": {
"executive_summary": {...},
"reviewer_feedback": {...},
"thematic_analysis": {...},
"specific_recommendations": {...},
"final_assessment": {...}
}
}
```
## Setup
1. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Configure OpenAI API key:
- Create a `.env` file in the project root
- Add your OpenAI API key:
```
OPENAI_API_KEY=your-api-key-here
```
3. Run the system:
```bash
python src/test_editor.py
```
## Usage
1. Initialize the editor agent:
```python
from editor_agent import EditorAgent
# Use default model (GPT-3.5-turbo)
editor = EditorAgent()
# Or specify custom model configuration
editor = EditorAgent({
"default": "gpt-3.5-turbo",
"production": "gpt-4-turbo-preview",
"current": "default"
})
```
2. Generate a review:
```python
result = editor.generate_review_plan(paper_content)
```
3. Switch models if needed:
```python
editor.set_model("production") # Switch to GPT-4
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.

8
Backup/V4_multi_agent/requirements.txt
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openai>=1.0.0
python-dotenv>=0.19.0
PyPDF2>=3.0.0
langchain>=0.1.0
langchain-community>=0.0.10
typing-extensions>=4.0.0
requests>=2.31.0
python-json-logger>=2.0.0

201
Backup/V4_multi_agent/src/coordinator_agent.py
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from typing import Dict, List, Any
import json
from openai_client import OpenAIClient
class CoordinatorAgent:
"""Coordinator agent for synthesizing specialized reviews."""
def __init__(self):
self.client = OpenAIClient()
self.paper_text = None # Store paper text for use across methods
def synthesize_reviews(self, specialized_reviews: Dict[str, Dict[str, Any]], paper_text: str) -> Dict[str, Any]:
"""Synthesize specialized reviews into a comprehensive report.
Args:
specialized_reviews (Dict[str, Dict[str, Any]]): Reviews from specialized agents
paper_text (str): The text content of the paper
Returns:
Dict[str, Any]: Comprehensive review report
"""
self.paper_text = paper_text # Store paper text for use in other methods
prompt = f"""Synthesize these specialized reviews into a comprehensive report.
Specialized Reviews:
{json.dumps(specialized_reviews, indent=2)}
Paper text:
{paper_text[:8000]} # Limit text length to avoid token limits
Provide a comprehensive synthesis in the following JSON format:
{{
"paper_overview": {{
"domain": "Main domain of the paper",
"key_contributions": ["List of key contributions"],
"technical_areas": ["List of technical areas covered"]
}},
"specialized_reviews": {{
"agent_id": {{
"expertise": ["List of expertise areas"],
"findings": "Summary of findings",
"recommendations": ["List of recommendations"]
}}
}},
"cross_domain_analysis": {{
"interdependencies": ["List of interdependencies between domains"],
"conflicts": ["List of conflicts between domains"],
"synergies": ["List of synergies between domains"]
}},
"comprehensive_assessment": {{
"ratings": {{
"technical_quality": {{"score": <1-10>, "justification": "text"}},
"methodology": {{"score": <1-10>, "justification": "text"}},
"innovation": {{"score": <1-10>, "justification": "text"}},
"clarity": {{"score": <1-10>, "justification": "text"}},
"ethics": {{"score": <1-10>, "justification": "text"}},
"impact": {{"score": <1-10>, "justification": "text"}}
}},
"key_strengths": ["List of key strengths"],
"key_weaknesses": ["List of key weaknesses"],
"critical_issues": ["List of critical issues"]
}},
"action_plan": {{
"priority_actions": ["List of priority actions"],
"timeline": "Estimated timeline for improvements",
"resource_requirements": ["List of resource requirements"]
}},
"final_recommendation": {{
"decision": "Accept/Minor Revision/Major Revision/Reject",
"justification": "Detailed justification",
"next_steps": ["List of next steps"]
}}
}}
Ensure your response is valid JSON and includes all required fields."""
try:
response = self.client.analyze_manuscript(paper_text, {
"role": "expert scientific editor synthesizing specialized reviews",
"task": "synthesize reviews",
"prompt": prompt
})
if "error" in response:
raise Exception(f"Failed to synthesize reviews: {response['error']}")
# Extract JSON from response if needed
if isinstance(response, str):
try:
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
response = json.loads(response[start_idx:end_idx])
else:
raise ValueError("No JSON found in response")
except json.JSONDecodeError as e:
raise Exception(f"Failed to parse JSON response: {str(e)}")
return response
except Exception as e:
print(f"Error synthesizing reviews: {e}")
return {
"error": "Failed to synthesize reviews",
"details": str(e)
}
def generate_final_report(self, specialized_reviews: Dict[str, Dict[str, Any]], synthesis: Dict[str, Any]) -> Dict[str, Any]:
"""Generate the final comprehensive report.
Args:
specialized_reviews (Dict[str, Dict[str, Any]]): Reviews from specialized agents
synthesis (Dict[str, Any]): Synthesis of reviews
Returns:
Dict[str, Any]: Final comprehensive report
"""
if not self.paper_text:
raise ValueError("Paper text not available. Call synthesize_reviews first.")
prompt = f"""Generate a final comprehensive report combining specialized reviews and synthesis.
Specialized Reviews:
{json.dumps(specialized_reviews, indent=2)}
Synthesis:
{json.dumps(synthesis, indent=2)}
Paper text:
{self.paper_text[:8000]} # Limit text length to avoid token limits
Provide the final report in the following JSON format:
{{
"executive_summary": {{
"paper_overview": "Brief overview of the paper",
"key_findings": ["List of key findings"],
"recommendation": "Final recommendation"
}},
"detailed_reviews": {{
"agent_id": {{
"expertise": ["List of expertise areas"],
"detailed_analysis": "Detailed analysis",
"specific_recommendations": ["List of specific recommendations"]
}}
}},
"cross_domain_analysis": {{
"interdependencies": ["List of interdependencies"],
"conflicts": ["List of conflicts"],
"synergies": ["List of synergies"]
}},
"comprehensive_assessment": {{
"overall_quality": 0,
"key_strengths": ["List of key strengths"],
"key_weaknesses": ["List of key weaknesses"],
"critical_issues": ["List of critical issues"]
}},
"action_plan": {{
"priority_actions": ["List of priority actions"],
"timeline": "Estimated timeline",
"resource_requirements": ["List of resource requirements"]
}},
"final_recommendation": {{
"decision": "Accept/Minor Revision/Major Revision/Reject",
"justification": "Detailed justification",
"next_steps": ["List of next steps"]
}}
}}
Ensure your response is valid JSON and includes all required fields."""
try:
response = self.client.analyze_manuscript(self.paper_text, {
"role": "expert scientific editor generating final report",
"task": "generate final report",
"prompt": prompt
})
if "error" in response:
raise Exception(f"Failed to generate final report: {response['error']}")
# Extract JSON from response if needed
if isinstance(response, str):
try:
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
response = json.loads(response[start_idx:end_idx])
else:
raise ValueError("No JSON found in response")
except json.JSONDecodeError as e:
raise Exception(f"Failed to parse JSON response: {str(e)}")
return response
except Exception as e:
print(f"Error generating final report: {e}")
return {
"error": "Failed to generate final report",
"details": str(e)
}

970
Backup/V4_multi_agent/src/editor_agent.py
View File

@@ -1,970 +0,0 @@
from typing import Dict, List, Any
import json
import os
from openai_client import OpenAIClient
class EditorAgent:
"""Editor agent that analyzes papers and creates specialized review teams."""
def __init__(self, model_config: Dict[str, str] = None):
"""Initialize the editor agent with configurable model settings.
Args:
model_config (Dict[str, str], optional): Configuration for different models. Defaults to:
{
"default": "gpt-3.5-turbo", # Cheap model for testing
"production": "gpt-4-turbo-preview", # More expensive model for production
"current": "default" # Which model to use currently
}
"""
try:
self.client = OpenAIClient()
except Exception as e:
raise RuntimeError(f"Failed to initialize OpenAI client: {str(e)}")
# Default model configuration
self.model_config = {
"default": "gpt-3.5-turbo", # Cheap model for testing
"production": "gpt-4-turbo-preview", # More expensive model for production
"current": "default" # Which model to use currently
}
# Update with any provided configuration
if model_config:
self.model_config.update(model_config)
# Core reviewers that are always included
self.core_reviewers = [
{
"id": "audience_terminology_reviewer",
"role": "Audience and Terminology Expert",
"expertise": [
"Audience Analysis",
"Field-Specific Terminology",
"Technical Language",
"Communication Strategy"
],
"focus_areas": [
"Audience Appropriateness",
"Terminology Usage",
"Technical Language Clarity",
"Communication Effectiveness"
],
"review_criteria": [
"Audience targeting",
"Terminology accuracy",
"Technical language appropriateness",
"Communication strategy effectiveness"
],
"required_background": [
"Audience analysis",
"Field-specific terminology",
"Technical communication",
"Communication strategies"
]
},
{
"id": "grammar_structure_reviewer",
"role": "Grammar and Structure Expert",
"expertise": [
"Grammar",
"Sentence Structure",
"Paragraph Organization",
"Text Flow"
],
"focus_areas": [
"Grammatical Correctness",
"Sentence Construction",
"Paragraph Coherence",
"Text Flow and Transitions"
],
"review_criteria": [
"Grammar accuracy",
"Sentence structure",
"Paragraph organization",
"Text flow and coherence"
],
"required_background": [
"Grammar rules",
"Sentence construction",
"Paragraph organization",
"Text flow principles"
]
},
{
"id": "spelling_mechanics_reviewer",
"role": "Spelling and Mechanics Expert",
"expertise": [
"Spelling",
"Punctuation",
"Capitalization",
"Formatting"
],
"focus_areas": [
"Spelling Accuracy",
"Punctuation Usage",
"Capitalization Rules",
"Formatting Consistency"
],
"review_criteria": [
"Spelling correctness",
"Punctuation accuracy",
"Capitalization appropriateness",
"Formatting consistency"
],
"required_background": [
"Spelling rules",
"Punctuation guidelines",
"Capitalization standards",
"Formatting conventions"
]
},
{
"id": "visual_presentation_reviewer",
"role": "Visual Presentation Expert",
"expertise": [
"Figure Design",
"Table Layout",
"Visual Communication",
"Publication Format"
],
"focus_areas": [
"Figure Clarity and Design",
"Table Organization",
"Visual Communication Effectiveness",
"Publication Format Compliance"
],
"review_criteria": [
"Figure clarity and design",
"Table organization",
"Visual communication effectiveness",
"Publication format compliance"
],
"required_background": [
"Figure design principles",
"Table layout standards",
"Visual communication",
"Publication guidelines"
]
},
{
"id": "literature_review_expert",
"role": "Literature Review Expert",
"expertise": [
"Literature Synthesis",
"Citation Analysis",
"Reference Management",
"Literature Coverage"
],
"focus_areas": [
"Citation Accuracy",
"Literature Coverage",
"Reference Consistency",
"Literature Gaps"
],
"review_criteria": [
"Citation completeness",
"Literature coverage",
"Reference formatting",
"Literature gap identification"
],
"required_background": [
"Citation standards",
"Literature review methodologies",
"Reference management",
"Literature analysis"
]
},
{
"id": "data_analysis_expert",
"role": "Data Analysis Expert",
"expertise": [
"Statistical Methods",
"Data Interpretation",
"Analytical Rigor",
"Statistical Validity"
],
"focus_areas": [
"Statistical Validity",
"Data Interpretation Accuracy",
"Analytical Methods",
"Statistical Rigor"
],
"review_criteria": [
"Statistical appropriateness",
"Data interpretation quality",
"Analytical rigor",
"Statistical validity"
],
"required_background": [
"Statistical analysis",
"Data interpretation",
"Analytical methodologies",
"Statistical validation"
]
},
{
"id": "results_presentation_expert",
"role": "Results Presentation Expert",
"expertise": [
"Results Organization",
"Data Visualization",
"Findings Presentation",
"Results Clarity"
],
"focus_areas": [
"Results Clarity",
"Data Presentation",
"Findings Organization",
"Results Impact"
],
"review_criteria": [
"Results presentation quality",
"Data visualization effectiveness",
"Findings clarity",
"Results impact"
],
"required_background": [
"Results presentation",
"Data visualization",
"Scientific communication",
"Impact assessment"
]
},
{
"id": "discussion_quality_expert",
"role": "Discussion Quality Expert",
"expertise": [
"Discussion Depth",
"Interpretation Quality",
"Implications Analysis",
"Discussion Structure"
],
"focus_areas": [
"Discussion Thoroughness",
"Interpretation Accuracy",
"Implications Clarity",
"Discussion Flow"
],
"review_criteria": [
"Discussion quality",
"Interpretation depth",
"Implications presentation",
"Discussion structure"
],
"required_background": [
"Discussion methodologies",
"Interpretation frameworks",
"Implications analysis",
"Discussion structure"
]
},
{
"id": "conclusion_strength_expert",
"role": "Conclusion Strength Expert",
"expertise": [
"Conclusion Formulation",
"Summary Quality",
"Future Directions",
"Conclusion Impact"
],
"focus_areas": [
"Conclusion Clarity",
"Summary Completeness",
"Future Directions Relevance",
"Conclusion Impact"
],
"review_criteria": [
"Conclusion strength",
"Summary quality",
"Future directions appropriateness",
"Conclusion impact"
],
"required_background": [
"Conclusion writing",
"Summary techniques",
"Future research planning",
"Impact assessment"
]
},
{
"id": "abstract_quality_expert",
"role": "Abstract Quality Expert",
"expertise": [
"Abstract Writing",
"Summary Skills",
"Key Points Extraction",
"Abstract Structure"
],
"focus_areas": [
"Abstract Clarity",
"Summary Completeness",
"Key Points Presentation",
"Abstract Impact"
],
"review_criteria": [
"Abstract quality",
"Summary accuracy",
"Key points clarity",
"Abstract impact"
],
"required_background": [
"Abstract writing",
"Summary techniques",
"Key points extraction",
"Impact assessment"
]
},
{
"id": "introduction_quality_expert",
"role": "Introduction Quality Expert",
"expertise": [
"Introduction Writing",
"Context Setting",
"Research Gap Identification",
"Introduction Structure"
],
"focus_areas": [
"Introduction Clarity",
"Context Completeness",
"Research Gap Presentation",
"Introduction Flow"
],
"review_criteria": [
"Introduction quality",
"Context setting",
"Research gap clarity",
"Introduction flow"
],
"required_background": [
"Introduction writing",
"Context setting",
"Research gap identification",
"Introduction structure"
]
},
{
"id": "limitations_analysis_expert",
"role": "Limitations Analysis Expert",
"expertise": [
"Limitations Identification",
"Constraint Analysis",
"Boundary Assessment",
"Limitations Impact"
],
"focus_areas": [
"Limitations Completeness",
"Constraint Clarity",
"Boundary Definition",
"Limitations Impact"
],
"review_criteria": [
"Limitations coverage",
"Constraint presentation",
"Boundary clarity",
"Limitations impact"
],
"required_background": [
"Limitations analysis",
"Constraint assessment",
"Boundary definition",
"Impact assessment"
]
},
{
"id": "future_work_expert",
"role": "Future Work Expert",
"expertise": [
"Future Research Planning",
"Extension Identification",
"Direction Setting",
"Future Impact"
],
"focus_areas": [
"Future Work Clarity",
"Extension Relevance",
"Direction Appropriateness",
"Future Impact"
],
"review_criteria": [
"Future work quality",
"Extension value",
"Direction clarity",
"Future impact"
],
"required_background": [
"Future research planning",
"Extension assessment",
"Direction setting",
"Impact assessment"
]
},
{
"id": "cross_reference_expert",
"role": "Cross-Reference Expert",
"expertise": [
"Cross-Reference Accuracy",
"Internal Consistency",
"Reference Linking",
"Reference Management"
],
"focus_areas": [
"Cross-Reference Completeness",
"Internal Consistency",
"Reference Linking",
"Reference Accuracy"
],
"review_criteria": [
"Cross-reference accuracy",
"Internal consistency",
"Reference linking quality",
"Reference accuracy"
],
"required_background": [
"Cross-reference standards",
"Internal consistency",
"Reference linking",
"Reference management"
]
},
{
"id": "methodology_reviewer",
"role": "Research Methodology Expert",
"expertise": [
"Research Design",
"Statistical Analysis",
"Experimental Methods",
"Data Collection",
"Reproducibility"
],
"focus_areas": [
"Research Design Quality",
"Statistical Rigor",
"Experimental Protocol",
"Data Collection Methods",
"Reproducibility Standards"
],
"review_criteria": [
"Methodological soundness",
"Statistical analysis appropriateness",
"Experimental design quality",
"Data collection rigor",
"Reproducibility potential"
],
"required_background": [
"Research methodology",
"Statistical analysis",
"Experimental design",
"Data collection standards",
"Reproducibility frameworks"
]
},
{
"id": "ethics_compliance_reviewer",
"role": "Ethics and Compliance Expert",
"expertise": [
"Research Ethics",
"Data Privacy",
"Informed Consent",
"Conflict of Interest",
"Regulatory Compliance"
],
"focus_areas": [
"Ethical Considerations",
"Data Protection",
"Participant Rights",
"Conflict Management",
"Regulatory Requirements"
],
"review_criteria": [
"Ethical compliance",
"Data privacy measures",
"Informed consent process",
"Conflict of interest disclosure",
"Regulatory adherence"
],
"required_background": [
"Research ethics",
"Data protection regulations",
"Human subjects research",
"Conflict of interest guidelines",
"Research compliance"
]
},
{
"id": "technical_implementation_reviewer",
"role": "Technical Implementation Expert",
"expertise": [
"Technical Architecture",
"Implementation Quality",
"Code/Algorithm Review",
"System Design",
"Performance Optimization"
],
"focus_areas": [
"Technical Architecture",
"Implementation Details",
"Code/Algorithm Quality",
"System Design",
"Performance Considerations"
],
"review_criteria": [
"Technical architecture soundness",
"Implementation quality",
"Code/algorithm efficiency",
"System design appropriateness",
"Performance optimization"
],
"required_background": [
"Software architecture",
"Implementation best practices",
"Code review standards",
"System design principles",
"Performance optimization"
]
},
{
"id": "impact_significance_reviewer",
"role": "Impact and Significance Expert",
"expertise": [
"Field Impact Assessment",
"Scientific Contribution",
"Practical Applications",
"Future Research Directions",
"Knowledge Advancement"
],
"focus_areas": [
"Scientific Impact",
"Field Contribution",
"Practical Relevance",
"Future Implications",
"Knowledge Gap Addressing"
],
"review_criteria": [
"Scientific contribution significance",
"Field impact potential",
"Practical application value",
"Future research implications",
"Knowledge advancement"
],
"required_background": [
"Impact assessment",
"Scientific contribution evaluation",
"Practical application analysis",
"Future research trends",
"Knowledge gap identification"
]
},
{
"id": "literature_coverage_expert",
"role": "Literature Coverage Expert",
"expertise": [
"Literature Comprehensiveness",
"Related Work Analysis",
"Citation Completeness",
"Field Coverage",
"Recent Developments"
],
"focus_areas": [
"Literature Coverage Completeness",
"Related Work Analysis",
"Citation Appropriateness",
"Field Coverage Breadth",
"Recent Literature Integration"
],
"review_criteria": [
"Literature coverage completeness",
"Related work analysis depth",
"Citation appropriateness",
"Field coverage breadth",
"Recent literature integration"
],
"required_background": [
"Literature review methodologies",
"Citation analysis",
"Field coverage assessment",
"Recent developments tracking",
"Related work analysis"
]
},
{
"id": "research_gap_contribution_expert",
"role": "Research Gap and Contribution Expert",
"expertise": [
"Research Gap Analysis",
"Contribution Positioning",
"Literature Synthesis",
"Field Impact Assessment",
"Academic Diplomacy"
],
"focus_areas": [
"Research Gap Identification",
"Contribution Significance",
"Literature Positioning",
"Field Impact Balance",
"Academic Tone Management"
],
"review_criteria": [
"Research gap clarity and justification",
"Contribution significance and uniqueness",
"Literature positioning accuracy",
"Field impact balance",
"Academic tone appropriateness"
],
"required_background": [
"Research gap analysis methodologies",
"Contribution assessment frameworks",
"Literature synthesis techniques",
"Field impact evaluation",
"Academic communication strategies"
]
}
]
def get_current_model(self) -> str:
"""Get the currently configured model to use."""
model_key = self.model_config["current"]
return self.model_config[model_key]
def set_model(self, model_key: str) -> None:
"""Set which model configuration to use.
Args:
model_key (str): Key of the model to use ("default" or "production")
"""
if model_key not in self.model_config:
raise ValueError(f"Unknown model key: {model_key}. Must be one of: {list(self.model_config.keys())}")
self.model_config["current"] = model_key
def analyze_paper(self, paper_text: str) -> Dict[str, Any]:
"""Analyze the paper to determine required expertise and review criteria.
Args:
paper_text (str): The text content of the paper
Returns:
Dict[str, Any]: Analysis of paper requirements and review team structure
"""
try:
response = self.client.analyze_manuscript(paper_text, {
"role": "expert scientific editor",
"task": "analyze paper",
"model": self.get_current_model(),
"prompt": """Analyze this scientific paper and provide:
1. Main domain and subdomains
2. Technical areas requiring expertise
3. Required review team composition
4. Key evaluation criteria
Return the analysis in JSON format with these exact keys:
{
"domains": {
"main_domain": "string",
"subdomains": ["string"]
},
"technical_areas": ["string"],
"required_expertise": ["string"],
"evaluation_criteria": ["string"]
}"""
})
if "error" in response:
raise Exception(response["error"])
return response
except Exception as e:
print(f"Error analyzing paper: {str(e)}")
return {
"error": "Failed to analyze paper",
"details": str(e)
}
def create_review_team(self, analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Create a specialized review team based on paper analysis.
Args:
analysis (Dict[str, Any]): Paper analysis from analyze_paper
Returns:
Dict[str, Any]: Review team configuration
"""
prompt = f"""Based on this paper analysis, create a specialized review team with specific expertise and focus areas.
Analysis:
{json.dumps(analysis, indent=2)}
Provide the review team configuration in the following JSON format:
{{
"review_team": {{
"agents": [
{{
"id": "unique_agent_id",
"role": "Specific role (e.g., Computer Vision Expert)",
"expertise": ["List of expertise areas"],
"focus_areas": ["List of focus areas"],
"review_criteria": ["List of specific criteria"],
"required_background": ["List of required background knowledge"]
}}
],
"review_process": {{
"sequence": ["Order of review steps"],
"dependencies": ["Review dependencies"],
"coordination_points": ["Points where agents need to coordinate"]
}}
}}
}}
Ensure your response is valid JSON and includes all required fields."""
try:
response = self.client.analyze_manuscript("", {
"role": "expert scientific editor",
"task": "create review team",
"analysis": analysis,
"prompt": prompt,
"model": self.get_current_model() # Use configured model
})
if "error" in response:
raise Exception(f"Failed to create review team: {response['error']}")
# Extract JSON from response if needed
if isinstance(response, str):
try:
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
response = json.loads(response[start_idx:end_idx])
else:
raise ValueError("No JSON found in response")
except json.JSONDecodeError as e:
raise Exception(f"Failed to parse JSON response: {str(e)}")
return response
except Exception as e:
print(f"Error creating review team: {e}")
return {
"error": "Failed to create review team",
"details": str(e)
}
def identify_key_scientists(self, paper_content: str) -> List[Dict[str, Any]]:
"""Identify key scientists in the field based on paper content and citations."""
try:
response = self.client.analyze_manuscript(paper_content, {
"role": "expert scientific editor",
"task": "identify key scientists",
"model": self.get_current_model(),
"prompt": """Identify the key scientists mentioned or relevant to this paper.
For each scientist provide:
{
"name": "string",
"research_focus": "string",
"review_style": "string",
"potential_concerns": ["string"],
"appreciated_areas": ["string"]
}
Return the list in JSON format."""
})
if "error" in response:
raise Exception(response["error"])
return response.get("scientists", [])
except Exception as e:
print(f"Error identifying scientists: {str(e)}")
return []
def simulate_scientist_feedback(self, paper_content: str, scientist: Dict[str, Any]) -> Dict[str, Any]:
"""Simulate feedback from a specific scientist."""
try:
response = self.client.analyze_manuscript(paper_content, {
"role": "expert scientific editor",
"task": "simulate scientist feedback",
"model": self.get_current_model(),
"prompt": f"""As {scientist['name']}, an expert in {scientist['research_focus']},
review this paper and provide feedback in this JSON format:
{{
"overall_assessment": "string",
"strengths": ["string"],
"concerns": ["string"],
"recommendations": ["string"],
"final_verdict": "string"
}}"""
})
if "error" in response:
raise Exception(response["error"])
return response
except Exception as e:
print(f"Error simulating feedback: {str(e)}")
return {
"error": f"Failed to simulate feedback for {scientist['name']}",
"details": str(e)
}
def generate_final_report(self, review_plan: Dict[str, Any]) -> Dict[str, Any]:
"""Generate a comprehensive final report including all reviewer feedback.
Args:
review_plan (Dict[str, Any]): The complete review plan including all feedback
Returns:
Dict[str, Any]: Comprehensive final report
"""
try:
response = self.client.analyze_manuscript(str(review_plan), {
"role": "expert scientific editor",
"task": "generate final report",
"model": self.get_current_model(),
"prompt": """Generate a comprehensive final report in this JSON format:
{
"executive_summary": {
"overall_assessment": "string",
"key_strengths": ["string"],
"key_concerns": ["string"],
"main_recommendations": ["string"]
},
"thematic_analysis": {
"common_themes": ["string"],
"conflicting_feedback": ["string"],
"consensus_points": ["string"]
},
"final_assessment": {
"overall_rating": "string",
"publication_readiness": "string",
"required_changes": ["string"],
"estimated_timeline": "string"
}
}"""
})
if "error" in response:
raise Exception(response["error"])
return response
except Exception as e:
print(f"Error generating final report: {str(e)}")
return {
"error": "Failed to generate final report",
"details": str(e)
}
def generate_review_plan(self, paper_content: str) -> Dict[str, Any]:
"""Generate a complete review plan including simulated expert feedback."""
try:
# Get basic paper analysis
paper_analysis = self.analyze_paper(paper_content)
if "error" in paper_analysis:
raise Exception(paper_analysis["error"])
# Identify key scientists
key_scientists = self.identify_key_scientists(paper_content)
# Simulate feedback from each scientist
simulated_feedback = {}
for scientist in key_scientists:
feedback = self.simulate_scientist_feedback(paper_content, scientist)
if "error" not in feedback:
simulated_feedback[scientist['name']] = feedback
# Generate the complete review plan
review_plan = {
"paper_analysis": paper_analysis,
"key_scientists": key_scientists,
"simulated_feedback": simulated_feedback
}
# Generate the final report
final_report = self.generate_final_report(review_plan)
return {
"review_plan": review_plan,
"final_report": final_report
}
except Exception as e:
print(f"Error generating review plan: {str(e)}")
return {
"error": "Failed to generate review plan",
"details": str(e),
"review_plan": {
"paper_analysis": paper_analysis if 'paper_analysis' in locals() else {"error": "Analysis failed"},
"key_scientists": key_scientists if 'key_scientists' in locals() else [],
"simulated_feedback": simulated_feedback if 'simulated_feedback' in locals() else {}
},
"final_report": {
"error": "Report generation failed",
"executive_summary": {"error": "Generation failed"},
"thematic_analysis": {"error": "Generation failed"},
"final_assessment": {"error": "Generation failed"}
}
}
def get_agent_feedback(self, agent_name, paper):
"""Get detailed feedback from a specific specialized reviewer agent."""
# Find the agent in the core reviewers
agent = next((a for a in self.core_reviewers if a['id'] == agent_name), None)
if not agent:
return {"error": f"Agent {agent_name} not found in review team"}
# Create the prompt for the specific agent
prompt = f"""As a {agent['role']} reviewer specializing in {', '.join(agent['expertise'])},
please provide detailed feedback on the following paper:
{paper}
Focus specifically on:
{', '.join(agent['review_criteria'])}
Provide your feedback in the following JSON format:
{{
"overall_assessment": "Detailed assessment of the paper from your expertise perspective",
"strengths": [
"List of specific strengths identified"
],
"areas_for_improvement": [
"List of areas needing improvement"
],
"specific_recommendations": [
"List of actionable recommendations"
],
"final_verdict": "Your final verdict on the paper"
}}
Base your review on your expertise in {', '.join(agent['required_background'])}.
Ensure your response is valid JSON.
"""
# Get the feedback from the agent
response = self.client.analyze_manuscript(paper, {
"role": "expert scientific editor",
"task": "get agent feedback",
"model": self.get_current_model(),
"prompt": prompt
})
# Extract JSON from response if needed
if isinstance(response, str):
try:
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
response = json.loads(response[start_idx:end_idx])
except json.JSONDecodeError:
response = {"error": "Failed to parse feedback"}
return {
"agent_name": agent_name,
"role": agent['role'],
"expertise": agent['expertise'],
"focus_areas": agent['focus_areas'],
"feedback": response
}

113
Backup/V4_multi_agent/src/openai_client.py
View File

@@ -1,113 +0,0 @@
import os
from typing import Dict, Any
from openai import OpenAI
from dotenv import load_dotenv, find_dotenv
import json
import re
class OpenAIClient:
"""Client for interacting with OpenAI's API."""
def __init__(self):
"""Initialize the OpenAI client."""
# Load environment variables from .env file
env_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env')
print(f"\nLooking for .env file at: {env_path}")
print(f"File exists: {os.path.exists(env_path)}")
# Try to load the .env file
if load_dotenv(env_path):
print("Successfully loaded .env file")
else:
print("Failed to load .env file")
# Get API key
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY not found in environment variables")
else:
# Mask most of the API key for security
masked_key = f"{api_key[:7]}...{api_key[-4:]}"
print(f"Found API key: {masked_key}")
try:
self.client = OpenAI(api_key=api_key)
print("Successfully initialized OpenAI client")
except Exception as e:
raise ValueError(f"Failed to initialize OpenAI client: {str(e)}")
def _extract_json_from_text(self, text: str) -> Dict[str, Any]:
"""Extract JSON from text that might contain other content."""
# Find the first { and last } to extract the JSON object
start = text.find('{')
end = text.rfind('}')
if start == -1 or end == -1:
return {"error": "No JSON found in response"}
json_str = text[start:end+1]
# Clean up common JSON formatting issues
json_str = re.sub(r',(\s*[}\]])', r'\1', json_str) # Remove trailing commas
json_str = re.sub(r'\\n\s*', ' ', json_str) # Remove newlines
json_str = re.sub(r'\s+', ' ', json_str) # Normalize whitespace
try:
return json.loads(json_str)
except json.JSONDecodeError as e:
return {
"error": "Failed to parse JSON",
"details": str(e),
"raw_text": text
}
def analyze_manuscript(self, content: str, params: Dict[str, Any]) -> Dict[str, Any]:
"""Analyze manuscript content using OpenAI's API."""
try:
# Print request details for debugging
print(f"\nMaking API call with model: {params.get('model', 'gpt-3.5-turbo')}")
print(f"Task: {params.get('task', 'unknown')}")
# Construct the messages for the chat completion
messages = [
{
"role": "system",
"content": (
f"You are an {params.get('role', 'expert scientific editor')}. "
"Your response must be a valid JSON object. "
"Do not include any text outside the JSON object. "
"Do not use markdown formatting. "
"Ensure all property names and string values are properly quoted."
)
},
{
"role": "user",
"content": (
f"{params.get('prompt', '')}\n\n"
"Remember to return ONLY a valid JSON object with no additional text.\n\n"
f"Content:\n{content}"
)
}
]
# Make the API call
response = self.client.chat.completions.create(
model=params.get('model', 'gpt-3.5-turbo'),
messages=messages,
temperature=0.3,
response_format={"type": "json_object"} # Request JSON response
)
# Extract and parse the response
response_text = response.choices[0].message.content
return self._extract_json_from_text(response_text)
except Exception as e:
return {
"error": f"API call failed: {str(e)}",
"details": {
"model": params.get('model', 'gpt-3.5-turbo'),
"role": params.get('role', 'expert scientific editor'),
"exception": str(e)
}
}

263
Backup/V4_multi_agent/src/specialized_agent.py
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@@ -1,263 +0,0 @@
from typing import Dict, List, Any
import json
from openai_client import OpenAIClient
class SpecializedReviewAgent:
"""Specialized review agent for domain-specific paper evaluation."""
def __init__(self, agent_config: Dict[str, Any]):
"""Initialize the specialized review agent.
Args:
agent_config (Dict[str, Any]): Agent configuration including role and expertise
"""
self.client = OpenAIClient()
self.role = agent_config["role"]
self.expertise = agent_config["expertise"]
self.focus_areas = agent_config["focus_areas"]
self.review_criteria = agent_config["review_criteria"]
self.criteria_description = agent_config.get("criteria_description", "Review criteria for this domain")
self.required_background = agent_config["required_background"]
def _create_review_template(self) -> Dict[str, Any]:
"""Create the review template based on agent's expertise.
Returns:
Dict[str, Any]: Review template structure
"""
return {
"agent_info": {
"role": self.role,
"expertise": self.expertise,
"focus_areas": self.focus_areas
},
"technical_review": {
"methodology_assessment": "...",
"technical_quality": "...",
"innovation_analysis": "...",
"comparative_analysis": "..."
},
"domain_specific_analysis": {
"strengths": [],
"weaknesses": [],
"improvements": [],
"technical_recommendations": []
},
"score": {
"value": 0,
"justification": "...",
"comparative_context": "..."
},
"detailed_feedback": {
"critical_issues": [],
"suggestions": [],
"references": []
}
}
def perform_review(self, paper_text: str) -> Dict[str, Any]:
"""Perform a specialized review of the paper.
Args:
paper_text (str): The text content of the paper
Returns:
Dict[str, Any]: Detailed review results
"""
prompt = f"""You are an expert {self.role} with deep knowledge in {', '.join(self.expertise)}.
Review this paper focusing on your areas of expertise and the following criteria:
Focus Areas:
{json.dumps(self.focus_areas, indent=2)}
Review Criteria Description:
{self.criteria_description}
Specific Review Criteria:
{json.dumps(self.review_criteria, indent=2)}
Required Background Knowledge:
{json.dumps(self.required_background, indent=2)}
Paper text:
{paper_text[:8000]} # Limit text length to avoid token limits
Provide your review in the following JSON format:
{
"strengths": [
{
"point": "Specific strength identified",
"location": "Section/paragraph where this strength appears",
"explanation": "Detailed explanation of why this is a strength",
"impact": "How this strength contributes to the paper's quality"
}
],
"weaknesses": [
{
"point": "Specific weakness identified",
"location": "Section/paragraph where this weakness appears",
"explanation": "Detailed explanation of why this is a weakness",
"impact": "How this weakness affects the paper's quality"
}
],
"improvements": [
{
"area": "Specific area needing improvement",
"current_state": "Description of the current state",
"suggestion": "Detailed, actionable suggestion for improvement",
"example": "Specific example or reference to support the suggestion",
"expected_impact": "How this improvement would enhance the paper"
}
],
"summary": {
"overall_assessment": "Overall assessment of the paper from your expertise perspective",
"key_points": ["Key points that need attention"],
"priority_improvements": ["Most important improvements to address first"]
}
}
Ensure your response is valid JSON and includes all required fields. Provide specific, detailed feedback with concrete examples and actionable suggestions."""
try:
response = self.client.analyze_manuscript(paper_text, {
"role": f"expert {self.role}",
"expertise": self.expertise,
"focus_areas": self.focus_areas,
"review_criteria": self.review_criteria,
"criteria_description": self.criteria_description,
"required_background": self.required_background,
"prompt": prompt
})
if "error" in response:
raise Exception(f"Failed to perform review: {response['error']}")
# Extract JSON from response if needed
if isinstance(response, str):
try:
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
response = json.loads(response[start_idx:end_idx])
else:
raise ValueError("No JSON found in response")
except json.JSONDecodeError as e:
raise Exception(f"Failed to parse JSON response: {str(e)}")
# Add agent info to response
response["agent_info"] = {
"role": self.role,
"expertise": self.expertise,
"focus_areas": self.focus_areas
}
return response
except Exception as e:
print(f"Error performing review: {e}")
return {
"error": "Failed to perform review",
"details": str(e),
"agent_info": {
"role": self.role,
"expertise": self.expertise,
"focus_areas": self.focus_areas
}
}
def provide_detailed_feedback(self, paper_text: str, initial_review: Dict[str, Any]) -> Dict[str, Any]:
"""Provide detailed feedback based on initial review.
Args:
paper_text (str): The text content of the paper
initial_review (Dict[str, Any]): Initial review results
Returns:
Dict[str, Any]: Detailed feedback
"""
prompt = f"""Based on your initial review, provide detailed feedback focusing on your expertise areas.
Initial Review:
{json.dumps(initial_review, indent=2)}
Review Criteria Description:
{self.criteria_description}
Specific Review Criteria:
{json.dumps(self.review_criteria, indent=2)}
Paper text:
{paper_text[:8000]} # Limit text length to avoid token limits
Provide detailed feedback in the following JSON format:
{{
"detailed_analysis": {{
"technical_details": "In-depth technical analysis",
"methodology_issues": "Detailed methodology issues",
"improvement_suggestions": "Specific improvement suggestions"
}},
"specific_recommendations": [
{{
"area": "Specific area",
"issue": "Detailed issue description",
"suggestion": "Specific suggestion",
"expected_impact": "Expected impact of change"
}}
],
"references_and_examples": [
{{
"reference": "Relevant reference",
"application": "How it applies to this paper",
"suggestion": "How to incorporate it"
}}
]
}}
Ensure your response is valid JSON and includes all required fields."""
try:
response = self.client.analyze_manuscript(paper_text, {
"role": f"expert {self.role} providing detailed feedback",
"expertise": self.expertise,
"focus_areas": self.focus_areas,
"review_criteria": self.review_criteria,
"criteria_description": self.criteria_description,
"initial_review": initial_review,
"prompt": prompt
})
if "error" in response:
raise Exception(f"Failed to provide detailed feedback: {response['error']}")
# Extract JSON from response if needed
if isinstance(response, str):
try:
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
response = json.loads(response[start_idx:end_idx])
else:
raise ValueError("No JSON found in response")
except json.JSONDecodeError as e:
raise Exception(f"Failed to parse JSON response: {str(e)}")
# Add agent info to response
response["agent_info"] = {
"role": self.role,
"expertise": self.expertise,
"focus_areas": self.focus_areas
}
return response
except Exception as e:
print(f"Error providing detailed feedback: {e}")
return {
"error": "Failed to provide detailed feedback",
"details": str(e),
"agent_info": {
"role": self.role,
"expertise": self.expertise,
"focus_areas": self.focus_areas
}
}

105
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@@ -1,105 +0,0 @@
from editor_agent import EditorAgent
import json
import os
def main():
# Initialize the editor agent with explicit configuration for the cheaper model
model_config = {
"default": "gpt-3.5-turbo", # Cheaper model for testing
"production": "gpt-4-turbo-preview", # More expensive model (not used in testing)
"current": "default" # Always use the cheaper model for testing
}
editor = EditorAgent(model_config)
print(f"\nUsing model: {editor.get_current_model()} (Testing Mode)")
# Sample paper content (short version for testing)
sample_paper = """
Title: A Novel Approach to Machine Learning in Healthcare
Abstract:
This paper presents a new machine learning framework for healthcare applications,
focusing on improved patient outcome prediction. We demonstrate significant
improvements over existing methods in terms of accuracy and interpretability.
Introduction:
Machine learning in healthcare has seen rapid development in recent years.
However, current approaches often lack interpretability and fail to consider
the unique challenges of medical data. Our work addresses these limitations
through a novel architecture that combines deep learning with domain-specific
constraints.
Related Work:
Previous approaches (Smith et al., 2020; Jones et al., 2021) have focused
primarily on prediction accuracy, often sacrificing interpretability. Recent
work by Brown et al. (2022) attempted to address this trade-off but faced
limitations in handling missing data.
Methods:
We propose a new neural network architecture that incorporates medical domain
knowledge through constrained optimization. Our approach uses a combination of
attention mechanisms and traditional statistical methods to ensure both
accuracy and interpretability.
Results:
Experimental results on three large-scale medical datasets show that our
method achieves a 15% improvement in prediction accuracy while maintaining
full interpretability. We also demonstrate superior handling of missing data
compared to existing approaches.
Discussion:
Our results suggest that the proposed framework successfully addresses the
limitations of current approaches. The improved accuracy and interpretability
make it particularly suitable for clinical applications.
"""
# Generate review plan
print("\nGenerating review plan...")
result = editor.generate_review_plan(sample_paper)
# Debug: Print review plan structure
print("\nReview plan structure:")
print(json.dumps(result, indent=2))
# Collect feedback from all specialized reviewers
print("\nCollecting specialized reviewer feedback...")
specialized_feedback = {}
for agent in editor.core_reviewers:
agent_feedback = editor.get_agent_feedback(agent['id'], sample_paper)
specialized_feedback[agent['id']] = agent_feedback
print(f"Collected feedback from {agent['role']}")
# Add specialized feedback to results
result['specialized_reviewer_feedback'] = specialized_feedback
# Create results directory if it doesn't exist
results_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'results')
os.makedirs(results_dir, exist_ok=True)
# Save complete results to JSON file
results_file = os.path.join(results_dir, 'review_results.json')
with open(results_file, 'w') as f:
json.dump(result, f, indent=2)
print(f"\nComplete results saved to: {results_file}")
# Print key parts of the result in a structured way
print("\n=== Paper Analysis ===")
print(json.dumps(result["review_plan"]["paper_analysis"], indent=2))
print("\n=== Key Scientists Identified ===")
for scientist in result["review_plan"]["key_scientists"]:
print(f"\nScientist: {scientist['name']}")
print(f"Research Focus: {scientist['research_focus']}")
print(f"Review Style: {scientist['review_style']}")
print("\n=== Executive Summary ===")
print(json.dumps(result["final_report"]["executive_summary"], indent=2))
print("\n=== Thematic Analysis ===")
print(json.dumps(result["final_report"]["thematic_analysis"], indent=2))
print("\n=== Final Assessment ===")
print(json.dumps(result["final_report"]["final_assessment"], indent=2))
if __name__ == "__main__":
main()

122
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@@ -1,122 +0,0 @@
# Contributing to Manuscript Reviewer
Thank you for considering contributing to Manuscript Reviewer! This document provides guidelines and instructions for contributing to the project.
## Project Status
**Important**: This project is currently in Version 1.0 (Beta), developed in a short timeframe. Expect hallucinations and errors. About 50% of feedback will likely be unusable, 30% mediocre, and 20% helpful. This is precisely why we need your contributions!
## How Can I Contribute?
### 1. Developing New Agents
We welcome contributions of new specialized agents:
- **Domain-specific agents**: Create agents specialized for specific research fields (e.g., medicine, computer science, social sciences)
- **Methodology agents**: Develop agents to assess specialized methodologies (e.g., clinical trials, machine learning, qualitative research)
- **Statistical review agents**: Create agents to validate complex statistical approaches
### 2. Improving Existing Agents
Help enhance our current agents:
- **Prompt engineering**: Refine agent prompts for better analysis
- **Error detection**: Improve the ability to identify common errors
- **Response formatting**: Enhance the structure and clarity of agent feedback
### 3. System Improvements
Contribute to the core system:
- **PDF parsing**: Enhance text and figure extraction
- **Report generation**: Improve the readability and usefulness of reports
- **Performance optimization**: Make the system faster and more efficient
- **UI/UX**: Build interfaces for easier interaction with the system
### 4. Documentation and Testing
Help make the project more accessible:
- **Documentation**: Improve installation and usage instructions
- **Tutorials**: Create examples and tutorials for different use cases
- **Testing**: Develop comprehensive tests for different components
## Getting Started
1. **Set up your environment**:
```bash
git clone https://github.com/robertjakob/rigorous.git
cd rigorous/V5_multi_agent2
pip install -r requirements.txt
pip install -e . # Install in development mode
```
2. **Configure API keys**:
Create a `.env` file based on the example and add your API keys if using OpenAI models.
3. **Test the system**:
```bash
# Place a PDF in the manuscripts directory
python run_analysis.py
# Generate a report
bash scripts/generate_report.sh
```
## Development Workflow
1. **Create a new branch**:
```bash
git checkout -b feature/your-feature-name
```
2. **Make your changes**:
- Follow the existing code style
- Add appropriate comments
- Keep functions focused and modular
3. **Test your changes**:
```bash
pytest
```
4. **Submit a pull request**:
- Provide a clear description of your changes
- Reference any related issues
- Explain how your changes improve the project
## Code Standards
- Use clear, descriptive variable and function names
- Follow PEP 8 style guidelines
- Include docstrings for all functions and classes
- Write unit tests for new functionality
## Adding a New Agent
1. Create a new file in the appropriate directory:
- `src/reviewer_agents/section/` for section agents
- `src/reviewer_agents/rigor/` for rigor agents
- `src/reviewer_agents/writing/` for writing agents
2. Inherit from the `BaseReviewerAgent` class:
```python
from src.reviewer_agents.base_agent import BaseReviewerAgent
class YourNewAgentName(BaseReviewerAgent):
def __init__(self, model="gpt-3.5-turbo"):
super().__init__(model)
self.name = "YourAgentCode" # e.g., S11, R8, W9
self.category = "your_category" # e.g., "section", "rigor", "writing"
def analyze_your_feature(self, text, research_type=None):
# Implement your analysis method
# Return a properly structured response
```
3. Add your agent to the controller in `src/reviewer_agents/controller_agent.py`
## Communication
- **Issues**: Use GitHub issues for bug reports and feature requests
- **Discussions**: Join GitHub discussions for general questions
- **Email**: Contact rjakob@ethz.ch for private communications
## Thank You
Your contributions help improve scientific publishing and research quality. We appreciate your support in this mission!

108
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@@ -1,108 +0,0 @@
# Model Configuration Guide
This guide explains how to configure different AI models for use with the Manuscript Reviewer system.
## Default Configuration
By default, the system uses ChatGPT 3.5 Turbo (`gpt-3.5-turbo`), which provides a good balance between performance and cost. However, for more sophisticated analysis, you may want to use more powerful models like GPT-4.
## Available Models
Here are some models you can use with this system:
| Model | Description | Pros | Cons |
|-------|-------------|------|------|
| `gpt-3.5-turbo` | Default model | Fast, cost-effective | Less sophisticated analysis |
| `gpt-4` | More powerful model | More accurate, better reasoning | Slower, more expensive |
| `gpt-4-turbo` | Updated GPT-4 | Newer capabilities, faster than GPT-4 | More expensive than GPT-3.5 |
| `claude-3-opus-20240229` | Claude 3 Opus | Alternative to GPT-4, different strengths | Requires Anthropic API key |
| `claude-3-sonnet-20240229` | Claude 3 Sonnet | Good balance of performance and speed | Requires Anthropic API key |
## Setting Up Your API Keys
### OpenAI API Keys
1. Create an account at [OpenAI](https://platform.openai.com/signup)
2. Navigate to the [API Keys page](https://platform.openai.com/api-keys)
3. Create a new API key
4. Copy the key to your `.env` file (see below)
### Anthropic API Keys (for Claude models)
1. Create an account at [Anthropic](https://console.anthropic.com/signup)
2. Navigate to the API Keys section
3. Create a new API key
4. Copy the key to your `.env` file (see below)
## Configuring Your Model
### Using the `.env` File
1. Open the `.env` file in the root directory of the project
2. Update the `OPENAI_API_KEY` with your API key
3. Change the `DEFAULT_MODEL` to your preferred model
Example `.env` file for OpenAI GPT-4:
```
# OpenAI API Configuration
OPENAI_API_KEY=your_openai_api_key_here
# Model Configuration
DEFAULT_MODEL=gpt-4
```
Example `.env` file for Anthropic Claude:
```
# Anthropic API Configuration
ANTHROPIC_API_KEY=your_anthropic_api_key_here
# Model Configuration
DEFAULT_MODEL=claude-3-opus-20240229
```
### Command Line Configuration
You can also specify the model when running the analysis:
```bash
# For OpenAI models
python run_analysis.py --model gpt-4
# For Anthropic models
python run_analysis.py --model claude-3-sonnet-20240229
```
## Performance Considerations
- **GPT-4** generally provides the most thorough analysis but can be slower and more expensive
- **GPT-3.5 Turbo** is much faster and cheaper, but may miss subtle issues
- **Claude 3 models** provide a good alternative to OpenAI models with different strengths
## Cost Management
Running these models incurs costs based on the number of tokens processed:
| Model | Approximate Cost per Full Paper Analysis |
|-------|------------------------------------------|
| GPT-3.5 Turbo | $0.10 - $0.25 |
| GPT-4 | $0.75 - $2.00 |
| GPT-4 Turbo | $0.40 - $1.00 |
| Claude 3 Opus | $0.80 - $2.20 |
| Claude 3 Sonnet | $0.30 - $0.80 |
Costs vary based on manuscript length and complexity.
## Troubleshooting
- If you encounter `API key not valid` errors, check that you've correctly copied your API key
- If you get `Model not found` errors, ensure you're using a valid model identifier
- For rate limit errors, you may need to wait or switch to a different model
## Need Help?
If you need assistance with model configuration, please:
- Check the [GitHub repository](https://github.com/robertjakob/rigorous) for updates
- Open an issue on GitHub for technical problems
- Contact us at rjakob@ethz.ch for specific questions

203
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@@ -1,203 +0,0 @@
# Manuscript Reviewer
A multi-agent system for comprehensive manuscript review and analysis.
## Overview
This project implements a sophisticated multi-agent system for reviewing and analyzing academic manuscripts. The system uses a combination of section-specific, rigor, and writing quality agents to provide detailed feedback and suggestions for improvement.
## Agent Structure
The system consists of three main categories of agents:
### Section Agents (S1-S10)
- S1: Title and Keywords Analysis
- S2: Abstract Review
- S3: Introduction Assessment
- S4: Literature Review Analysis
- S5: Methodology Evaluation
- S6: Results Analysis
- S7: Discussion Review
- S8: Conclusion Assessment
- S9: References Analysis
- S10: Supplementary Materials Review
### Rigor Agents (R1-R7)
- R1: Originality and Contribution
- R2: Impact and Significance
- R3: Ethics and Compliance
- R4: Data and Code Availability
- R5: Statistical Rigor
- R6: Technical Accuracy
- R7: Consistency
### Writing Agents (W1-W8)
- W1: Clarity
- W2: Organization
- W3: Grammar
- W4: Style
- W5: Technical Accuracy
- W6: Consistency
- W7: Readability
- W8: Overall Writing Quality
## Installation
1. Clone the repository
2. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Place your manuscript PDF in the `manuscripts/` directory
2. Run the analysis:
```bash
python run_analysis.py
```
For information on using more powerful models (like GPT-4), see [MODEL_GUIDE.md](MODEL_GUIDE.md).
## Output
The system generates a comprehensive report in `results/manuscript_report.md` containing:
- Overall assessment
- Section-by-section analysis
- Critical remarks
- Improvement suggestions
- Detailed feedback
- Summary of findings
For information on using more powerful models (like GPT-4), see [MODEL_GUIDE.md](MODEL_GUIDE.md).
## Report Generator
The report generator component takes the combined output from all agents and creates a well-structured markdown report.
### Report Structure
1. **Header**
- Title and generation timestamp
- Important notes about the tool's status
- Overall assessment summary
2. **Section Analysis (S1-S10)**
- Title and Keywords through Supplementary Materials
3. **Rigor Analysis (R1-R7)**
- Originality, Impact, Ethics, Data Availability, etc.
4. **Writing Quality (W1-W8)**
- Language, Structure, Clarity, Terminology, etc.
### Agent Response Format
Each agent's analysis follows a consistent JSON structure:
```json
{
"score": int, // Score from 1-10
"critical_remarks": [
{
"category": str,
"location": str,
"issue": str,
"severity": str,
"impact": str
}
],
"improvement_suggestions": [
{
"location": str,
"category": str,
"focus": str,
"original_text": str,
"improved_version": str,
"explanation": str
}
],
"detailed_feedback": {
// Agent-specific detailed analysis
},
"summary": str // Overall assessment summary
}
```
### Customization
The report template and formatting can be modified in:
- `src/core/report_template.py`: Main report structure
- `src/utils/json_to_report.py`: JSON to markdown conversion
## Configuration
- Environment variables are managed in `.env`
- Agent configurations can be modified in `src/config/`
- Logging settings in `src/config/logging_config.py`
## Development
### Project Structure
```
V5_multi_agent2/
├── src/
│ ├── reviewer_agents/
│ │ ├── section/ # Section agents (S1-S10)
│ │ ├── rigor/ # Rigor agents (R1-R7)
│ │ ├── writing/ # Writing agents (W1-W8)
│ │ └── controller_agent.py
│ ├── core/
│ ├── utils/
│ └── config/
├── manuscripts/ # Input manuscripts
├── results/ # Generated reports
└── tests/ # Test suite
```
### Adding New Agents
1. Create a new agent class inheriting from `BaseReviewerAgent`
2. Implement the required analysis method
3. Add the agent to the controller's agent dictionary
4. Update the report template if needed
## Testing
Run the test suite:
```bash
pytest tests/
```
## License
MIT License
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
For detailed guidelines on how to contribute, please see [CONTRIBUTING.md](CONTRIBUTING.md).
## Join the Project
**We Need Your Help!** This is Version 1.0 (Beta) - a work in progress developed over just a few days, which means:
- **Expect imperfections**: About 50% of feedback may be unusable, 30% mediocre, and 20% genuinely helpful
- **Your expertise matters**: Help us improve agent accuracy, especially specialized agents
- **Key areas for contribution**:
- Developing specialized agents for different research fields
- Improving prompt engineering for existing agents
- Enhancing report generation and visualization
- Adding support for different document formats
- Implementing more sophisticated error detection
**Share your feedback**: Contact us at rjakob@ethz.ch with your experiences and suggestions
**Use more powerful models**: The default implementation uses ChatGPT 3.5 for accessibility, but you can configure the system to use more sophisticated models like GPT-4 with your own API keys.
Together, we can build the best review agent team and improve the quality of scientific publishing!

18
Backup/V5_multi_agent2/requirements.txt
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@@ -1,18 +0,0 @@
PyPDF2>=3.0.0
PyMuPDF>=1.22.0 # fitz
Pillow>=10.0.0 # PIL
pytesseract>=0.3.10
numpy>=1.24.0
openai>=1.0.0
python-dotenv>=0.19.0
langchain>=0.1.0
langchain-community>=0.0.10
typing-extensions>=4.0.0
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{
"originality_contribution_score": 8,
"critical_remarks": [
{
"category": "novelty",
"location": "Abstract / Introduction",
"issue": "While the study claims to extend prior work by predicting nonadherence over longer durations and in different interventions, the core methodology\u2014using behavioral app engagement data with machine learning\u2014is established in existing literature. The novelty lies mainly in application scope rather than methodological innovation.",
"severity": "medium",
"impact": "This limits the perceived novelty of the approach, potentially affecting the paper's contribution perception, though the extended application adds value."
},
{
"category": "contribution",
"location": "Discussion / Results",
"issue": "The paper emphasizes the potential for targeted interventions based on predictions but does not empirically test or demonstrate the effectiveness of such strategies, leaving the practical impact somewhat speculative.",
"severity": "high",
"impact": "This reduces the strength of the contribution claim regarding real-world impact and limits the evidence for practical implementation."
},
{
"category": "verification",
"location": "Introduction / Methods",
"issue": "The claims of high predictive accuracy are based on retrospective data, with no prospective validation or real-time testing of the models' utility in live interventions.",
"severity": "high",
"impact": "This affects the validity of the claims about the models' real-world applicability and robustness."
},
{
"category": "comparison",
"location": "Introduction / Literature Review",
"issue": "The comparison with existing literature is somewhat superficial, lacking detailed discussion of how this work advances beyond similar predictive models in digital health or app domains.",
"severity": "medium",
"impact": "This limits the contextual understanding of the study's novelty and its relative contribution."
},
{
"category": "advancement",
"location": "Discussion / Conclusion",
"issue": "The paper states that the models 'advance knowledge' but does not clearly specify how this advances theoretical understanding or practical capabilities beyond existing models.",
"severity": "medium",
"impact": "This weakens the framing of the study as a significant knowledge advancement."
}
],
"improvement_suggestions": [
{
"original_text": "We developed machine learning models for the prediction of nonadherence in two mHealth interventions...",
"improved_version": "We applied and adapted existing machine learning frameworks to predict nonadherence across two distinct mHealth interventions, demonstrating their scalability and generalizability.",
"explanation": "Clarifies that the contribution is in application and validation across contexts, emphasizing generalizability rather than methodological novelty.",
"location": "Abstract",
"category": "novelty",
"focus": "novelty"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira...",
"improved_version": "Our models achieved high predictive accuracy in identifying nonadherent users over extended periods, comparable to or exceeding existing models, thereby confirming their robustness in real-world settings.",
"explanation": "Highlights the performance relative to existing models, framing the contribution as validation and robustness rather than novelty.",
"location": "Results",
"category": "contribution",
"focus": "contribution"
},
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted...",
"improved_version": "Building upon prior research, this study investigates the extent to which behavioral app engagement data can predict nonadherence over longer durations, filling a notable gap in the literature.",
"explanation": "Explicitly states the research gap addressed, clarifying the novelty of the investigation.",
"location": "Introduction",
"category": "verification",
"focus": "verification"
},
{
"original_text": "Our study thus extends prior research showing that methodologies effective in predicting churn during the first week remain applicable over longer durations...",
"improved_version": "This study extends prior research by demonstrating that predictive methodologies effective in early churn detection remain applicable over longer durations, thereby broadening their practical utility.",
"explanation": "Clarifies how the work advances existing methods, emphasizing practical extension rather than methodological innovation.",
"location": "Discussion",
"category": "advancement",
"focus": "advancement"
},
{
"original_text": "We selected generalizable daily app engagement features related to users' activity and progress...",
"improved_version": "We selected simple, objective behavioral features\u2014such as daily app activity and task completion\u2014that are readily available across diverse mHealth apps, supporting their broad applicability.",
"explanation": "Highlights the simplicity and generalizability of features, strengthening the contribution claim.",
"location": "Methods",
"category": "contribution",
"focus": "contribution"
},
{
"original_text": "The models predicted nonadherence relative to the intended use, following Sieverink et al. (2017)...",
"improved_version": "Our operationalization of nonadherence aligns with established frameworks (Sieverink et al., 2017), ensuring consistency with current standards and facilitating comparison with future studies.",
"explanation": "Strengthens the verification of the novelty claim by anchoring it to recognized definitions and standards.",
"location": "Introduction",
"category": "verification",
"focus": "verification"
},
{
"original_text": "The study demonstrates that nonadherence can be accurately predicted over extended durations...",
"improved_version": "While our retrospective analysis shows promising predictive performance over extended durations, prospective validation is necessary to confirm real-world utility and impact.",
"explanation": "Provides a balanced view, acknowledging limitations and emphasizing the need for further validation, which enhances research credibility.",
"location": "Discussion",
"category": "verification",
"focus": "verification"
},
{
"original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted...",
"improved_version": "Our findings demonstrate that behavioral app engagement data can serve as reliable predictors of nonadherence, contributing to the growing evidence base for data-driven adherence management in digital health.",
"explanation": "Frames the contribution as adding to the evidence base, emphasizing the practical utility rather than claiming groundbreaking novelty.",
"location": "Abstract / Discussion",
"category": "advancement",
"focus": "advancement"
},
{
"original_text": "The models' performance improves as more data become available, aligning with prior research...",
"improved_version": "The observed improvement in model performance with increased data availability aligns with prior findings in app engagement research, reinforcing the robustness of behavioral features for long-term prediction.",
"explanation": "Connects findings to existing literature, clarifying the contribution as validation and reinforcement of known principles.",
"location": "Results / Discussion",
"category": "comparison",
"focus": "comparison"
},
{
"original_text": "Our approach relies on behavioral engagement data, which is typically not available in non-digital interventions...",
"improved_version": "This approach leverages objective behavioral data unique to digital interventions, highlighting the advantage of digital health platforms in enabling precise adherence prediction.",
"explanation": "Clarifies the unique contribution enabled by digital data, strengthening the novelty argument.",
"location": "Discussion",
"category": "novelty",
"focus": "novelty"
},
{
"original_text": "Future work should include prospective trials to validate these models in real-world settings...",
"improved_version": "Future research should focus on prospective, real-time validation of these models to establish their effectiveness in guiding adaptive adherence interventions and improving health outcomes.",
"explanation": "Provides a clear pathway for advancing the research, emphasizing practical impact and validation.",
"location": "Discussion / Conclusion",
"category": "advancement",
"focus": "advancement"
}
],
"detailed_feedback": {
"novelty_assessment": "The study primarily extends existing methodologies of churn and nonadherence prediction by applying them over longer durations and in different medical contexts. While the application scope broadens, the core predictive approach\u2014using behavioral app engagement data with machine learning\u2014is well-established. The novelty is thus moderate, centered on demonstrating generalizability and robustness rather than methodological innovation.",
"contribution_analysis": "The paper contributes valuable evidence that behavioral engagement features can reliably predict nonadherence over extended periods, supporting their utility in diverse mHealth settings. It emphasizes the potential for integrating these models into intervention workflows for proactive adherence support, although it stops short of empirically testing intervention efficacy. The work advances practical understanding but could strengthen its contribution by demonstrating actual intervention impacts.",
"verification_status": "The claims of high predictive accuracy are based on retrospective analyses of large datasets, which are promising but require prospective validation to confirm real-world applicability. The models' performance in live settings remains to be tested, and the paper appropriately acknowledges this limitation.",
"comparative_analysis": "The discussion briefly references prior literature on churn prediction but lacks a detailed comparison of performance metrics, feature sets, or methodological differences. A more thorough comparison would clarify how this work advances the state of the art and what unique insights it offers.",
"advancement_evaluation": "The research advances knowledge by demonstrating the applicability of behavioral engagement data for long-term adherence prediction and by emphasizing the potential for scalable, data-driven intervention strategies. It reinforces the importance of simple, objective features and supports their use across diverse interventions, thus contributing to the practical toolkit for digital health researchers and practitioners."
},
"summary": "Overall, this study makes a solid contribution by validating and extending existing predictive models for nonadherence over longer durations and in different contexts. Its strengths lie in demonstrating the robustness and generalizability of behavioral engagement features. To enhance its impact, the paper should more explicitly delineate its novelty relative to prior work, empirically validate intervention benefits, and deepen comparisons with existing literature. The balanced acknowledgment of limitations and future directions adds credibility, positioning this work as a meaningful step toward more adaptive and scalable adherence management in digital health."
}

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{
"impact_significance_score": 8,
"critical_remarks": [
{
"category": "field_influence",
"location": "Abstract",
"issue": "While the abstract highlights the high accuracy of ML models in predicting nonadherence, it lacks explicit discussion on how these findings could shift current paradigms in digital health research.",
"severity": "medium",
"impact": "This limits the perceived novelty and potential influence of the research within the field, possibly underestimating its capacity to reshape adherence prediction methodologies."
},
{
"category": "implications",
"location": "Discussion, paragraph 4.1",
"issue": "The discussion emphasizes the predictive accuracy but insufficiently explores how these models could directly influence health outcomes or clinical decision-making processes.",
"severity": "high",
"impact": "This diminishes the broader significance of the findings, potentially limiting their translational impact on health policy and clinical practice."
},
{
"category": "future_research",
"location": "Discussion, paragraph 4.4",
"issue": "The limitations section notes the need for prospective trials but does not specify concrete research designs or intervention strategies to test the models' real-world efficacy.",
"severity": "high",
"impact": "This vagueness hampers the field\u2019s ability to build on these findings systematically, delaying the development of evidence-based implementation protocols."
},
{
"category": "applications",
"location": "Discussion, paragraph 4.2",
"issue": "While potential intervention strategies are mentioned, there is a lack of detailed discussion on how these predictive insights could be integrated into existing app architectures or clinical workflows.",
"severity": "medium",
"impact": "This reduces the practical utility of the research, limiting immediate translation into actionable app features or healthcare interventions."
},
{
"category": "policy",
"location": "Introduction and discussion",
"issue": "The paper briefly mentions regulatory environments but does not elaborate on how predictive models could influence health policy, reimbursement strategies, or regulatory approval processes.",
"severity": "medium",
"impact": "This oversight constrains the research\u2019s influence on policy development, potentially slowing adoption at the systemic level."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "The rich, objective engagement data collected by mHealth interventions have the potential to fundamentally transform adherence monitoring and prediction, offering new avenues for personalized intervention strategies.",
"explanation": "This reframes the statement to emphasize the transformative potential of the data, enhancing the perceived impact on the field.",
"location": "Abstract",
"category": "field_influence",
"focus": "field_influence"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95).",
"improved_version": "Our models demonstrated the capacity to accurately identify over 94% of nonadherent users across extended periods, indicating a significant advancement in early intervention potential within digital health research.",
"explanation": "This emphasizes the significance of the predictive accuracy as a breakthrough, strengthening its influence on future research and practice.",
"location": "Abstract",
"category": "field_influence",
"focus": "field_influence"
},
{
"original_text": "The study extends prior research showing that methodologies effective in predicting churn during the first week remain applicable over longer durations.",
"improved_version": "This study extends the frontier of adherence research by demonstrating that predictive methodologies are effective not only in early stages but also over prolonged intervention durations, paving the way for sustained engagement strategies.",
"explanation": "This highlights the novelty and importance of the extended prediction window, reinforcing the research\u2019s influence on future methodological developments.",
"location": "Discussion, paragraph 4.1",
"category": "field_influence",
"focus": "field_influence"
},
{
"original_text": "Models predicting churn achieved mean AUCs of 0.87 for both apps, correctly identifying 84-86% of churned users.",
"improved_version": "The high predictive performance (AUC ~0.87) in churn models underscores the feasibility of integrating real-time risk stratification into digital health platforms, potentially revolutionizing adherence management.",
"explanation": "This frames the results as a catalyst for systemic change, enhancing the research\u2019s influence on the field\u2019s evolution."
},
{
"original_text": "Feature importance analyses revealed that behavioral app engagement features collected closer to the prediction event had a stronger impact.",
"improved_version": "Findings that recent behavioral engagement features are most predictive underscore the importance of designing interventions that leverage near-real-time data, influencing future research and app development strategies.",
"explanation": "This directs the field towards designing adaptive, data-driven intervention models, increasing practical relevance."
},
{
"original_text": "The models\u2019 performance improved over time as more behavioral data became available.",
"improved_version": "The progressive enhancement in model accuracy over time highlights the potential for dynamic, continuously learning systems that adapt to evolving user behaviors, shaping future research in adaptive digital health solutions.",
"explanation": "This emphasizes innovation and future directions, boosting the research\u2019s impact on the development of intelligent, scalable interventions."
},
{
"original_text": "Our findings suggest that simple app engagement features are sufficient to predict future user behavior.",
"improved_version": "The demonstration that basic, objective engagement metrics can reliably predict adherence opens new avenues for scalable, low-cost monitoring systems applicable across diverse mHealth interventions.",
"explanation": "This enhances the perceived practicality and broad applicability, increasing the research\u2019s influence on implementation strategies."
},
{
"original_text": "Limitations include the reliance on rich behavioral data, which may not be available in all settings.",
"improved_version": "Future research should explore the integration of sparse or sporadic engagement data, broadening the applicability of predictive models to interventions with less frequent user interactions.",
"explanation": "This expands the scope, encouraging further innovation and impact across varied intervention contexts."
},
{
"original_text": "Prospective trials are necessary to fully establish the models\u2019 real-world utility.",
"improved_version": "Designing and executing prospective, randomized trials will be crucial to validate these models\u2019 effectiveness in real-world clinical and behavioral settings, accelerating their translation into practice.",
"explanation": "This provides a clear pathway for future impactful research, emphasizing translational potential."
},
{
"original_text": "The models could be integrated with targeted strategies such as personalized push notifications or content adaptations.",
"improved_version": "Integrating predictive models with tailored, in-app intervention strategies\u2014such as personalized notifications, adaptive content, and human support\u2014can significantly enhance adherence and health outcomes.",
"explanation": "This clarifies how models can directly influence practical intervention design, boosting their significance."
},
{
"original_text": "The study briefly mentions regulatory environments but does not elaborate on how predictive models could influence health policy.",
"improved_version": "Future work should investigate how predictive adherence models can inform health policy, reimbursement frameworks, and regulatory standards to facilitate widespread adoption and integration into healthcare systems.",
"explanation": "This explicitly links research findings to policy impact, increasing systemic influence."
},
{
"original_text": "The discussion emphasizes the predictive accuracy but insufficiently explores how these models could influence health outcomes or clinical decision-making.",
"improved_version": "By demonstrating high predictive accuracy, this research lays the groundwork for integrating adherence prediction into clinical workflows, potentially improving health outcomes through timely, personalized interventions.",
"explanation": "This explicitly connects model performance to tangible health and clinical benefits, enhancing broader significance."
},
{
"original_text": "The limitations section notes the need for prospective trials but does not specify concrete research designs.",
"improved_version": "Future research should implement randomized controlled trials and real-world pilot studies to evaluate the impact of predictive-driven adherence interventions on health outcomes and system efficiency.",
"explanation": "This provides concrete guidance for advancing the research impact through systematic validation."
}
],
"detailed_feedback": {
"field_influence": "This research significantly advances the field by demonstrating the robustness and generalizability of behavioral engagement data for long-term adherence prediction, setting a new standard for scalable, data-driven adherence monitoring in digital health.",
"broader_implications": "The findings suggest that objective, app-based behavioral data can fundamentally reshape adherence management, enabling personalized, timely interventions that could reduce the burden of noncommunicable diseases and optimize healthcare resource utilization globally.",
"future_research_impact": "Building on these results, future studies should focus on prospective validation, integration with clinical workflows, and testing intervention strategies that leverage real-time predictions to improve health outcomes and system efficiency.",
"practical_applications": "The models\u2019 reliance on simple behavioral features facilitates immediate integration into existing mHealth platforms, enabling proactive adherence support, personalized nudges, and cost-effective scaling of digital health interventions.",
"policy_implications": "The demonstrated potential for predictive adherence models to inform personalized intervention strategies underscores the need for policy frameworks that support data sharing, real-time monitoring, and reimbursement models aligned with digital health innovation."
},
"summary": "This study offers a compelling and methodologically rigorous contribution to digital health research, demonstrating that behavioral app engagement data can reliably predict nonadherence over extended periods. Its findings have the potential to influence future research directions, practical intervention designs, and health policy frameworks, although further prospective validation and implementation research are essential to realize its full systemic impact."
}

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{
"ethics_compliance_score": 7,
"critical_remarks": [
{
"category": "conflicts",
"location": "Author Contributions and Conflicts of Interest section",
"issue": "Authors disclose affiliations with organizations that have financial ties to health insurers and digital health companies, but the potential influence of these conflicts on study design, data interpretation, or reporting is not explicitly discussed.",
"severity": "medium",
"impact": "Potential bias in research outcomes or interpretation, which could compromise objectivity and transparency."
},
{
"category": "privacy",
"location": "Abstract and Methods sections",
"issue": "While datasets are anonymized, there is limited detail on specific data privacy measures, data storage, or security protocols implemented to protect user data during analysis.",
"severity": "medium",
"impact": "Insufficient transparency about data privacy measures may undermine trust and compliance with data protection standards."
},
{
"category": "consent",
"location": "Methods section, Dataset descriptions",
"issue": "The paper states that only users who provided consent under specific regulations were included, but it does not detail the process of obtaining informed consent or how users were informed about data use.",
"severity": "high",
"impact": "Lack of detailed consent procedures raises concerns about whether participants were adequately informed, which is essential for ethical compliance."
},
{
"category": "integrity",
"location": "Results and Discussion sections",
"issue": "The study reports high predictive performance but does not address potential biases in model training, such as class imbalance or overfitting, nor does it discuss validation on external datasets.",
"severity": "medium",
"impact": "Potential overestimation of model effectiveness, which could mislead stakeholders and affect ethical application of findings."
},
{
"category": "guidelines",
"location": "Ethics Declaration",
"issue": "The ethics statement claims exemption from human subjects review due to anonymized data, but it does not specify adherence to relevant ethical guidelines such as the Declaration of Helsinki or GDPR principles.",
"severity": "low",
"impact": "Insufficient detail on adherence to established ethical standards may weaken the ethical rigor of the study."
}
],
"improvement_suggestions": [
{
"original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.",
"improved_version": "The study explicitly states that informed consent was obtained from all participants in accordance with GDPR and local ethical standards, detailing the consent process, including how users were informed about data use and their rights.",
"explanation": "Providing detailed consent procedures enhances transparency and demonstrates adherence to ethical standards for participant autonomy and informed participation.",
"location": "Methods section, Dataset descriptions",
"category": "consent",
"focus": "consent"
},
{
"original_text": "The study involves the analyses of two anonymous datasets. Therefore, it does not qualify as human subject research and has been exempted from ethics approval by the research team's University Ethics Committee.",
"improved_version": "The study confirms that all data were anonymized prior to analysis, and that data handling complied with GDPR and local ethical guidelines, with documentation available upon request. Ethical approval was obtained for the use of identifiable data where applicable.",
"explanation": "Clarifying compliance with data protection laws and ethical standards reassures readers of ethical rigor and legal adherence.",
"location": "Ethics Declaration",
"category": "guidelines",
"focus": "guidelines"
},
{
"original_text": "Authors disclose affiliations with organizations that have financial ties to health insurers and digital health companies, but the potential influence of these conflicts on study design, data interpretation, or reporting is not explicitly discussed.",
"improved_version": "The authors declare their affiliations and funding sources, and explicitly state that these entities had no role in study design, data analysis, interpretation, or manuscript preparation to mitigate potential conflicts of interest.",
"explanation": "Explicitly stating the independence of the research process from funding sources enhances transparency and addresses potential conflicts.",
"location": "Conflicts of Interest section",
"category": "conflicts",
"focus": "conflicts"
},
{
"original_text": "While datasets are anonymized, there is limited detail on specific data privacy measures, data storage, or security protocols implemented to protect user data during analysis.",
"improved_version": "The study details the data privacy measures, including secure data storage, access controls, and compliance with GDPR, ensuring that user data remained protected throughout the research process.",
"explanation": "Providing specific privacy and security measures demonstrates commitment to data protection standards and ethical data management.",
"location": "Methods section, Data collection",
"category": "privacy",
"focus": "privacy"
},
{
"original_text": "The paper states that only users who provided consent under specific regulations were included, but it does not detail the process of obtaining informed consent or how users were informed about data use.",
"improved_version": "The manuscript describes the informed consent process, including how users were informed about data collection, purpose, storage, and their rights, in accordance with GDPR and local ethical standards.",
"explanation": "Detailing the consent process ensures transparency and confirms adherence to ethical standards for participant autonomy.",
"location": "Methods section, Consent procedures",
"category": "consent",
"focus": "consent"
},
{
"original_text": "The study reports high predictive performance but does not address potential biases in model training, such as class imbalance or overfitting, nor does it discuss validation on external datasets.",
"improved_version": "The discussion includes acknowledgment of potential biases such as class imbalance, and describes steps taken to mitigate overfitting, including cross-validation and external validation where applicable, to ensure model robustness.",
"explanation": "Addressing biases and validation enhances research integrity and trustworthiness of the findings.",
"location": "Results and Discussion",
"category": "integrity",
"focus": "integrity"
},
{
"original_text": "The ethics statement claims exemption from human subjects review due to anonymized data, but it does not specify adherence to relevant ethical guidelines such as the Declaration of Helsinki or GDPR principles.",
"improved_version": "The study affirms compliance with ethical guidelines including the Declaration of Helsinki and GDPR, with documentation of ethical considerations and data handling procedures available upon request.",
"explanation": "Explicitly referencing established ethical frameworks reinforces the ethical rigor of the research.",
"location": "Ethics Declaration",
"category": "guidelines",
"focus": "guidelines"
},
{
"original_text": "Authors affiliated with commercial entities (Pathmate Technologies AG, Vivira Health Lab GmbH) are involved, but the manuscript does not discuss potential influence or measures to prevent bias.",
"improved_version": "The manuscript discloses the authors' affiliations and states that independent oversight or measures, such as external validation or peer review, were employed to minimize potential bias arising from these affiliations.",
"explanation": "Transparency about measures to prevent bias from conflicts of interest enhances ethical standards and credibility.",
"location": "Author Contributions and Conflicts of Interest",
"category": "conflicts",
"focus": "conflicts"
},
{
"original_text": "The study emphasizes predictive accuracy but does not discuss the potential implications or risks of false positives/negatives in clinical or user contexts.",
"improved_version": "The discussion addresses the ethical implications of false positives and negatives, including potential risks such as unnecessary interventions or missed opportunities for support, and suggests strategies to mitigate these risks.",
"explanation": "Acknowledging potential harms and ethical considerations in model deployment ensures responsible application of research findings.",
"location": "Discussion",
"category": "integrity",
"focus": "guidelines"
},
{
"original_text": "The study does not specify whether any ethical review or oversight was conducted for the development and validation of predictive models beyond data anonymization.",
"improved_version": "The authors state that the development and validation of predictive models adhered to institutional ethical standards, with oversight from relevant committees, and that all procedures complied with applicable laws and guidelines.",
"explanation": "Clarifying oversight processes reinforces adherence to ethical standards in all research phases.",
"location": "Methods and Ethics Declaration",
"category": "guidelines",
"focus": "guidelines"
}
],
"detailed_feedback": {
"conflicts_assessment": "The authors disclose affiliations with organizations involved in digital health and insurance sectors, but do not explicitly discuss measures taken to prevent bias or influence. Transparency about independent oversight or measures to mitigate conflicts would strengthen ethical integrity.",
"privacy_compliance": "While datasets are anonymized, the manuscript lacks detailed description of data security, storage, and privacy measures aligned with GDPR. Explicit statements on these protocols would enhance confidence in data privacy compliance.",
"consent_procedures": "The paper mentions that only users who provided consent were included, but it does not detail the process of obtaining informed consent, how users were informed about data use, or their rights. Clearer description of consent procedures is necessary for ethical transparency.",
"research_integrity": "High predictive performance is reported, but potential biases such as class imbalance and overfitting are not addressed. Including validation strategies and discussing limitations would improve research integrity and trustworthiness.",
"guidelines_adherence": "The ethics declaration states exemption from review due to anonymized data but does not specify adherence to international ethical standards like the Declaration of Helsinki or GDPR principles. Explicit confirmation of compliance would strengthen ethical rigor."
},
"summary": "Overall, the study demonstrates a solid foundation in data analysis and ethical considerations, but could improve transparency regarding informed consent procedures, detailed data privacy measures, conflict of interest management, and explicit adherence to established ethical guidelines. Addressing these areas would enhance the ethical robustness and credibility of the research."
}

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{
"data_code_availability_score": 4,
"critical_remarks": [
{
"category": "data_sharing",
"location": "Abstract / Data Description",
"issue": "The datasets are described in detail, but there is no mention of publicly available data repositories or links where the data can be accessed by other researchers.",
"severity": "high",
"impact": "Lack of accessible data hampers reproducibility and independent validation of results, reducing transparency."
},
{
"category": "code_availability",
"location": "Methodology / Data Analysis",
"issue": "No mention of publicly available code repositories, scripts, or software used for analysis and modeling.",
"severity": "high",
"impact": "Absence of shared code limits reproducibility and impedes other researchers from verifying or building upon the work."
},
{
"category": "documentation",
"location": "Appendix / Methodology",
"issue": "Details about software packages and hyperparameter grids are referenced but not provided within the main text or accessible supplementary materials.",
"severity": "medium",
"impact": "Insufficient documentation reduces clarity for replication efforts and understanding of the modeling process."
},
{
"category": "restrictions",
"location": "Data / Ethics Declaration",
"issue": "Data use is limited to users who provided consent under specific regulations; no information on how others can access similar datasets or request data sharing.",
"severity": "medium",
"impact": "Restrictions limit external validation and broader reproducibility, especially for researchers outside the specific regulatory context."
},
{
"category": "reproducibility",
"location": "Discussion / Limitations",
"issue": "The paper emphasizes the potential for generalizability but lacks details on how to reproduce the predictive models in different settings or with different data.",
"severity": "medium",
"impact": "Limited guidance on reproducibility reduces the ability of others to validate and extend the findings."
}
],
"improvement_suggestions": [
{
"original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.",
"improved_version": "The datasets used in this study are not publicly available due to privacy and regulatory restrictions. However, de-identified versions or synthetic datasets mimicking the original data can be shared upon reasonable request and under data use agreements. Details are provided in the supplementary materials.",
"explanation": "Clarifies data sharing options within legal constraints, promoting transparency and enabling replication under controlled conditions.",
"location": "Data Description / Ethics Declaration",
"category": "data_sharing",
"focus": "data_sharing"
},
{
"original_text": "The dataset included data from 8,372 users who activated their first prescription between January 1, 2023, and March 31, 2024.",
"improved_version": "The datasets, including anonymized user engagement and outcome data, are available in a public repository such as [Repository Name], under accession number [XYZ], or upon request from the corresponding author. Access is subject to data use agreements to ensure privacy compliance.",
"explanation": "Provides concrete access pathways, facilitating external validation and further research.",
"location": "Data Description",
"category": "data_sharing",
"focus": "data_sharing"
},
{
"original_text": "No mention of publicly available code repositories.",
"improved_version": "All analysis scripts, preprocessing code, and modeling pipelines are openly available at [GitHub/Zenodo link], with detailed documentation on usage and dependencies.",
"explanation": "Ensures code transparency, enabling others to reproduce and build upon the analysis.",
"location": "Methodology / Data Analysis",
"category": "code_availability",
"focus": "code_availability"
},
{
"original_text": "Detailed hyperparameter grids are provided in Appendix 6.1.",
"improved_version": "Complete code for data preprocessing, feature extraction, model training, hyperparameter tuning, and evaluation, along with hyperparameter grids, are hosted in the public repository [Link], with step-by-step instructions in the README file.",
"explanation": "Enhances reproducibility by providing executable code and clear instructions.",
"location": "Appendix / Methodology",
"category": "code_availability",
"focus": "code_availability"
},
{
"original_text": "Details about software packages are referenced but not provided within the main text or accessible supplementary materials.",
"improved_version": "A comprehensive list of software packages, versions, and environment setup instructions are included in the repository's README and environment configuration files (e.g., environment.yml or requirements.txt).",
"explanation": "Facilitates environment replication, reducing setup barriers for other researchers.",
"location": "Appendix / Software environment",
"category": "documentation",
"focus": "documentation"
},
{
"original_text": "No explicit mention of code documentation or usage instructions.",
"improved_version": "All code repositories include detailed documentation, including data input formats, step-by-step analysis workflows, and instructions for reproducing the figures and results.",
"explanation": "Improves usability and transparency of the codebase for external users.",
"location": "Code Documentation",
"category": "documentation",
"focus": "code_availability"
},
{
"original_text": "The paper emphasizes the potential for generalizability but lacks details on how to reproduce models in different contexts.",
"improved_version": "We provide a reproducible pipeline with sample datasets, code, and detailed instructions in the supplementary materials, enabling researchers to adapt the models to new datasets or settings.",
"explanation": "Supports reproducibility and adaptation across different studies and populations.",
"location": "Discussion / Reproducibility",
"category": "reproducibility",
"focus": "reproducibility"
},
{
"original_text": "The datasets are described in detail, but no links or access procedures are provided.",
"improved_version": "Data sharing details, including access procedures, repository links, and licensing conditions, are provided in the Data Availability Statement and supplementary materials.",
"explanation": "Ensures transparency about data accessibility, encouraging external validation.",
"location": "Data Description",
"category": "data_sharing",
"focus": "data_sharing"
},
{
"original_text": "No mention of sharing analysis code or scripts.",
"improved_version": "All analysis scripts, including data preprocessing, feature engineering, and modeling code, are publicly available at [Repository Link], with comprehensive documentation and example workflows.",
"explanation": "Enables independent reproduction and validation of the results.",
"location": "Methodology / Data Analysis",
"category": "code_availability",
"focus": "code_availability"
},
{
"original_text": "The paper does not specify how to access supplementary materials or code repositories.",
"improved_version": "All supplementary materials, including code, hyperparameter configurations, and detailed methodological descriptions, are hosted at [Repository/DOI link], accessible to all researchers.",
"explanation": "Provides clear access points, promoting transparency and reproducibility.",
"location": "Conclusion / Data and Code Availability",
"category": "documentation",
"focus": "code_availability"
}
],
"detailed_feedback": {
"data_sharing_assessment": "While the datasets are described thoroughly, their availability is restricted due to privacy and regulatory considerations. To enhance transparency, the authors should specify whether de-identified or synthetic datasets are available and provide access procedures, such as data repositories or request protocols.",
"code_availability": "The manuscript lacks any mention of publicly accessible code repositories or scripts used for data analysis and modeling. Sharing code via platforms like GitHub or Zenodo, along with documentation, would significantly improve reproducibility and facilitate external validation.",
"documentation_completeness": "The current documentation references appendices and supplementary materials but does not include explicit instructions, software environment details, or step-by-step workflows. Providing comprehensive documentation, including environment setup files and usage instructions, would support reproducibility.",
"restrictions_justification": "Data access is limited by consent and regulatory restrictions, which are justified but should be clearly communicated. Providing pathways for data access under controlled conditions or sharing synthetic datasets can help balance privacy with transparency.",
"reproducibility_support": "The paper emphasizes the potential for generalizability but does not include executable code, detailed workflows, or environment specifications. Including these elements in public repositories would enable others to reproduce and adapt the models effectively."
},
"summary": "Overall, the manuscript demonstrates strong methodological rigor but falls short in openly sharing data and code, which are critical for transparency and reproducibility. Addressing these gaps by providing access to datasets (where permissible), sharing analysis scripts, and including detailed documentation would substantially enhance the research's transparency and impact."
}

160
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@@ -1,160 +0,0 @@
{
"statistical_rigor_score": 6,
"critical_remarks": [
{
"category": "test_selection",
"location": "Methodology: 2.2 Feature Selection, Model Training, and Evaluation",
"issue": "While random forest models are used extensively, there is limited discussion on the justification for choosing this algorithm over others, especially given the class imbalance and the potential benefits of alternative methods like boosting or neural networks.",
"severity": "medium",
"impact": "This may limit the exploration of potentially more optimal models, affecting the robustness and generalizability of the findings."
},
{
"category": "assumptions",
"location": "Methodology: 2.2 Data Preparation",
"issue": "There is no explicit mention of assumption checks for the machine learning models, such as feature independence, multicollinearity, or the distributional assumptions underlying the preprocessing steps.",
"severity": "medium",
"impact": "Unverified assumptions could lead to overfitting or biased models, reducing the validity of the predictive performance."
},
{
"category": "sample_size",
"location": "Methodology: 2.2 Model Training and Evaluation",
"issue": "Although large sample sizes are used, there is no formal sample size justification or power analysis to confirm that the datasets are sufficient for the complexity of the models and the number of features.",
"severity": "low",
"impact": "This omission may affect the confidence in the stability and reliability of the model estimates."
},
{
"category": "multiple_comparisons",
"location": "Results: 3 Prediction Results",
"issue": "Multiple performance metrics are reported across numerous weeks and days without correction for multiple testing or comparison, increasing the risk of Type I errors.",
"severity": "medium",
"impact": "This can lead to overstated significance and misinterpretation of the model's performance improvements over time."
},
{
"category": "effect_size",
"location": "Results: 3.2 Prediction Results",
"issue": "While performance metrics like AUC, accuracy, and F1 are reported, there is limited discussion on the effect sizes or practical significance of the predictive improvements.",
"severity": "low",
"impact": "Without effect size context, it's difficult to assess the real-world impact of the models' predictive capabilities."
},
{
"category": "confidence_intervals",
"location": "Results: 3.2 Prediction Results",
"issue": "Confidence intervals are not provided for key metrics such as AUC, accuracy, or F1 scores, which limits understanding of the precision of these estimates.",
"severity": "high",
"impact": "This omission hampers the assessment of statistical uncertainty and the robustness of the reported performance metrics."
},
{
"category": "p_value",
"location": "Discussion: 4.1 Predicting Nonadherence",
"issue": "There is no mention of p-values or statistical significance testing for the differences in model performance metrics over time or between models.",
"severity": "low",
"impact": "This limits the ability to statistically validate observed performance trends or differences."
},
{
"category": "power",
"location": "Methodology: 2.2 Model Training and Evaluation",
"issue": "No formal power analysis is conducted to determine whether the sample sizes and number of events are adequate for the model complexity and class imbalance.",
"severity": "low",
"impact": "Potential underpowering could lead to unstable estimates and reduced confidence in model performance."
},
{
"category": "missing_data",
"location": "Methodology: 2.1 Datasets and Definitions of Nonadherence",
"issue": "Handling of missing data is not explicitly described; it is unclear whether missing engagement or demographic data were imputed, excluded, or otherwise managed.",
"severity": "high",
"impact": "Unaddressed missing data can bias results and compromise the validity of the models."
},
{
"category": "outliers",
"location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence",
"issue": "There is no discussion on outlier detection or treatment for continuous variables such as number of exercises or sessions, which could skew model training.",
"severity": "medium",
"impact": "Unhandled outliers may distort model learning and reduce predictive accuracy."
}
],
"improvement_suggestions": [
{
"original_text": "While random forest models are used extensively, there is limited discussion on the justification for choosing this algorithm over others, especially given the class imbalance and the potential benefits of alternative methods like boosting or neural networks.",
"improved_version": "Include a rationale for selecting random forest algorithms, such as comparative performance analyses with other models like XGBoost, neural networks, or logistic regression, especially considering class imbalance and interpretability.",
"explanation": "Providing a comparative justification enhances the transparency and robustness of the model choice, ensuring that the selected method is optimal for the data characteristics.",
"location": "Methodology: 2.2 Model Training and Evaluation",
"category": "test_selection"
},
{
"original_text": "There is no explicit mention of assumption checks for the machine learning models, such as feature independence, multicollinearity, or the distributional assumptions underlying the preprocessing steps.",
"improved_version": "Add a section detailing assumption checks, such as correlation analyses for multicollinearity, distribution assessments for features, and validation of preprocessing steps like normalization or scaling.",
"explanation": "Verifying assumptions ensures the validity of the preprocessing and modeling steps, reducing bias and overfitting risks.",
"location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence",
"category": "assumptions"
},
{
"original_text": "Although large sample sizes are used, there is no formal sample size justification or power analysis to confirm that the datasets are sufficient for the complexity of the models and the number of features.",
"improved_version": "Incorporate a formal sample size and power analysis, considering the number of features, expected effect sizes, and class imbalance, to justify the adequacy of the datasets for model training.",
"explanation": "A formal justification enhances confidence that the study is adequately powered to detect meaningful differences and model performance levels.",
"location": "Methodology: 2.2 Model Training and Evaluation",
"category": "sample_size"
},
{
"original_text": "Multiple performance metrics are reported across numerous weeks and days without correction for multiple testing or comparison, increasing the risk of Type I errors.",
"improved_version": "Apply statistical correction methods such as Bonferroni or Holm adjustments when comparing performance metrics across multiple weeks or days, and report adjusted p-values to control for multiple comparisons.",
"explanation": "Correcting for multiple comparisons reduces false-positive findings, increasing the validity of performance trend interpretations.",
"location": "Results: 3 Prediction Results",
"category": "multiple_comparisons"
},
{
"original_text": "While performance metrics like AUC, accuracy, and F1 are reported, there is limited discussion on the effect sizes or practical significance of the predictive improvements.",
"improved_version": "Include effect size measures such as Cohen\u2019s d or differences in performance metrics with confidence intervals to contextualize the practical significance of improvements over time.",
"explanation": "Effect sizes provide a clearer understanding of the real-world impact of the predictive models beyond statistical significance alone.",
"location": "Results: 3.2 Prediction Results",
"category": "effect_size"
},
{
"original_text": "Confidence intervals are not provided for key metrics such as AUC, accuracy, or F1 scores, which limits understanding of the precision of these estimates.",
"improved_version": "Calculate and report 95% confidence intervals for all key performance metrics (AUC, accuracy, F1, precision, recall) to quantify the uncertainty around these estimates.",
"explanation": "Confidence intervals allow for assessment of the statistical precision and reliability of the performance metrics, strengthening the interpretability of results.",
"location": "Results: 3.2 Prediction Results",
"category": "confidence_intervals"
},
{
"original_text": "There is no mention of p-values or statistical significance testing for the differences in model performance metrics over time or between models.",
"improved_version": "Conduct formal statistical tests (e.g., paired t-tests, bootstrap comparisons) to evaluate whether observed differences in performance metrics across weeks or models are statistically significant, and report p-values accordingly.",
"explanation": "Statistical testing of performance differences enhances the rigor in claiming improvements or differences between models or time points.",
"location": "Discussion: 4.1 Predicting Nonadherence",
"category": "p_value"
},
{
"original_text": "No formal power analysis is conducted to determine whether the sample sizes and number of events are adequate for the model complexity and class imbalance.",
"improved_version": "Include a formal power analysis or sample size calculation tailored to the expected effect sizes, class imbalance, and model complexity to justify dataset adequacy.",
"explanation": "This ensures the study is sufficiently powered to detect meaningful differences and reduces the risk of underpowered analyses.",
"location": "Methodology: 2.2 Model Training and Evaluation",
"category": "power"
},
{
"original_text": "Handling of missing data is not explicitly described; it is unclear whether missing engagement or demographic data were imputed, excluded, or otherwise managed.",
"improved_version": "Describe the methods used for handling missing data, such as multiple imputation, exclusion criteria, or data augmentation, and justify their appropriateness.",
"explanation": "Explicit missing data handling prevents bias and ensures transparency in data preprocessing, improving the validity of the models.",
"location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence",
"category": "missing_data"
},
{
"original_text": "There is no discussion on outlier detection or treatment for continuous variables such as number of exercises or sessions, which could skew model training.",
"improved_version": "Implement outlier detection procedures (e.g., z-scores, IQR-based methods) and specify how outliers are treated or excluded to prevent distortion of model training.",
"explanation": "Proper outlier management enhances model stability and accuracy by reducing the influence of extreme values.",
"location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence",
"category": "outliers"
}
],
"detailed_feedback": {
"test_selection": "The study predominantly employs random forest algorithms for all predictions, citing prior success but not explicitly comparing alternative models. Incorporating a model comparison section, including simpler or more interpretable models like logistic regression or boosting algorithms, would strengthen the justification for the chosen approach and potentially improve predictive performance.",
"assumption_verification": "The preprocessing steps mention normalization and scaling but do not detail assumption checks such as multicollinearity assessments or feature independence. Explicitly conducting and reporting such checks ensures the data meet the assumptions underlying the preprocessing and modeling steps, reducing bias and overfitting risks.",
"sample_size_justification": "While large datasets are used, the absence of formal sample size calculations or power analyses limits confidence in the sufficiency of the data for complex ML models. Including such analyses would confirm that the study is adequately powered to detect meaningful differences and model performance levels.",
"multiple_comparison_handling": "Multiple performance metrics are reported across many time points without correction for multiple testing. Applying statistical correction methods like Bonferroni or Holm adjustments, and reporting adjusted p-values, would mitigate the risk of false-positive findings and enhance the robustness of performance trend claims.",
"effect_size_reporting": "The results focus on performance metrics without contextualizing their practical significance. Including effect size measures or confidence intervals for differences in performance over time would clarify the real-world impact of the models\u2019 improvements.",
"confidence_intervals": "Key metrics such as AUC, accuracy, and F1 scores are presented without confidence intervals. Calculating and reporting these intervals would provide insight into the precision and reliability of the estimates, strengthening the interpretability of the results.",
"p_value_interpretation": "The discussion does not include significance testing for performance differences. Conducting formal statistical tests and reporting p-values would allow for rigorous validation of observed trends and differences, reducing subjective interpretation.",
"statistical_power": "No formal power analysis is reported, which raises concerns about whether the sample sizes and number of events are sufficient for the complexity of the models, especially given class imbalance. Including power calculations would bolster confidence in the findings.",
"missing_data_handling": "The handling of missing data is not described, leaving uncertainty about potential biases introduced by data exclusion or imputation. Explicitly detailing missing data management strategies would improve transparency and validity.",
"outlier_treatment": "There is no discussion of outlier detection or treatment for continuous variables like exercise counts. Implementing outlier detection methods and describing their use would prevent skewed model training and improve accuracy."
},
"summary": "Overall, the study demonstrates a commendable effort in applying machine learning to predict nonadherence in mHealth interventions, with extensive datasets and multiple performance evaluations. However, enhancements in statistical rigor\u2014such as explicit assumption checks, correction for multiple testing, confidence interval reporting, and formal power analyses\u2014are needed to strengthen the validity and interpretability of the findings. Addressing these aspects will improve the robustness, reproducibility, and practical relevance of the research outcomes."
}

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@@ -1,178 +0,0 @@
{
"technical_accuracy_score": 8,
"critical_remarks": [
{
"category": "derivations",
"location": "Mathematical Framework section, paragraphs describing feature normalization and model training",
"issue": "While the text mentions normalization techniques like square root scaling and standard scaling, it lacks detailed mathematical derivations or formulas explaining the exact transformations applied to features, which could lead to ambiguity in reproducibility.",
"severity": "medium",
"impact": "This omission may hinder precise replication and understanding of the preprocessing steps, affecting the technical rigor."
},
{
"category": "algorithms",
"location": "Methodology, paragraph on model training and hyperparameter tuning",
"issue": "The choice of random forest is justified with references to prior studies, but there is no detailed discussion of hyperparameter settings, feature selection process, or validation procedures beyond stratified 10-fold cross-validation, which might impact the assessment of algorithm correctness and efficiency.",
"severity": "medium",
"impact": "Limited transparency on model tuning could obscure potential overfitting or underfitting issues, affecting the perceived robustness."
},
{
"category": "terminology",
"location": "Throughout the document, especially in definitions of adherence and churn",
"issue": "The terms 'nonadherence,' 'churn,' and 'disengagement' are used with nuanced distinctions, but some definitions are inconsistent or lack precise operationalization, e.g., 'discontinuing use' vs 'not meeting weekly exercise thresholds.'",
"severity": "low",
"impact": "Inconsistent terminology may cause confusion in interpreting results and applying the models in practice."
},
{
"category": "equations",
"location": "Mathematical Framework section, descriptions of feature normalization",
"issue": "The text mentions 'normalizing numerical data by performing square root scaling' but does not provide explicit formulas or equations illustrating the transformation, which could lead to ambiguity.",
"severity": "low",
"impact": "Lack of explicit equations reduces clarity and reproducibility."
},
{
"category": "completeness",
"location": "Methodology, model evaluation",
"issue": "While multiple performance metrics are reported, there is limited discussion on how class imbalance was addressed beyond Tomek Links undersampling, and no mention of other techniques like SMOTE or cost-sensitive learning.",
"severity": "medium",
"impact": "Incomplete coverage of imbalance handling methods may affect the interpretation of model performance and generalizability."
},
{
"category": "consistency",
"location": "Throughout the document, especially in definitions of prediction windows",
"issue": "Different definitions of prediction windows (e.g., 7-day, 31-day) are used across models and datasets without a unified framework or explicit rationale, which could lead to inconsistencies in interpretation.",
"severity": "low",
"impact": "This inconsistency can affect the logical comparability of results across models and datasets."
},
{
"category": "implementation",
"location": "Methodology, software packages and hyperparameter tuning",
"issue": "The appendix mentions the use of 'freely available Python packages' but does not specify versions, specific libraries, or code snippets, which hampers reproducibility and assessment of implementation correctness.",
"severity": "medium",
"impact": "Limited implementation transparency diminishes confidence in the reproducibility and technical rigor."
},
{
"category": "edge_cases",
"location": "Discussion, limitations",
"issue": "The text acknowledges that models rely on rich, continuous engagement data but does not explicitly address how the models perform with sparse or irregular data, or how they handle users with very short engagement periods.",
"severity": "high",
"impact": "Ignoring such edge cases could lead to overestimating model robustness and applicability in real-world, less ideal data scenarios."
},
{
"category": "complexity",
"location": "Technical analysis, model evaluation",
"issue": "There is minimal discussion on the computational complexity, training time, or resource requirements of the random forest models, which is critical for practical deployment in large-scale mHealth systems.",
"severity": "low",
"impact": "Lack of complexity analysis limits understanding of model scalability and operational feasibility."
},
{
"category": "documentation",
"location": "Overall, including appendices and references",
"issue": "The documentation of hyperparameter grids, feature importance analysis, and model evaluation procedures is limited to references to appendices, which are not fully detailed in the main text, reducing transparency.",
"severity": "medium",
"impact": "This hampers full understanding and independent validation of the technical methods used."
}
],
"improvement_suggestions": [
{
"original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.",
"improved_version": "The manuscript should explicitly specify the exact inclusion criteria, including the consent process and data anonymization procedures, to enhance clarity and reproducibility.",
"explanation": "Clearer operationalization of inclusion criteria improves transparency and allows other researchers to replicate the sample selection process.",
"location": "Methodology, Dataset description",
"category": "documentation",
"focus": "clarity"
},
{
"original_text": "We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning on the training sets, optimizing for F1 score for all models.",
"improved_version": "The hyperparameter tuning process should specify the parameter grid, number of iterations, and the criteria for selecting the best hyperparameters, along with details on how overfitting was prevented.",
"explanation": "Detailed hyperparameter procedures ensure reproducibility and allow assessment of the robustness of the tuning process.",
"location": "Methodology, model training",
"category": "implementation",
"focus": "clarity"
},
{
"original_text": "Normalization techniques like square root scaling and standard scaling are mentioned but lack explicit formulas.",
"improved_version": "Include explicit formulas, e.g., for square root scaling: x' = sqrt(x + 1), and specify the parameters used for standard scaling (mean and standard deviation).",
"explanation": "Providing formulas enhances clarity, reproducibility, and allows others to verify the correctness of preprocessing steps.",
"location": "Mathematical Framework, preprocessing",
"category": "equations",
"focus": "equations"
},
{
"original_text": "The models' performance metrics are reported, but the class imbalance handling methods are only briefly mentioned.",
"improved_version": "Describe in detail the class imbalance mitigation strategies employed, such as oversampling, undersampling, or cost-sensitive learning, and justify their choice.",
"explanation": "Comprehensive reporting of imbalance handling techniques clarifies how models maintain performance despite skewed classes.",
"location": "Methodology, evaluation",
"category": "completeness",
"focus": "completeness"
},
{
"original_text": "The definitions of adherence and churn are given, but the operational thresholds (e.g., 'fewer than eight exercises') could be further justified.",
"improved_version": "Provide empirical or clinical justification for the chosen thresholds, referencing prior studies or clinical guidelines where applicable.",
"explanation": "Justification of thresholds strengthens the validity of the operational definitions and their relevance to health outcomes.",
"location": "Introduction, definitions",
"category": "terminology",
"focus": "terminology"
},
{
"original_text": "The description of feature importance analyses is limited to appendix references.",
"improved_version": "Summarize key findings from feature importance analyses in the main text, including which features most strongly influence predictions and their potential clinical relevance.",
"explanation": "Enhances transparency and helps readers understand which behavioral factors drive model predictions.",
"location": "Results, feature importance",
"category": "documentation",
"focus": "clarity"
},
{
"original_text": "The discussion mentions the reliance on continuous engagement data but does not specify how models perform with sparse or irregular data.",
"improved_version": "Include an analysis or discussion on model robustness in scenarios with sparse data, possibly with sensitivity analyses or simulation studies.",
"explanation": "Addressing edge cases improves understanding of model applicability in real-world, less-than-ideal data conditions.",
"location": "Discussion, limitations",
"category": "edge_cases",
"focus": "edge_cases"
},
{
"original_text": "The complexity of the random forest models is not discussed.",
"improved_version": "Provide an analysis of computational complexity, including training time, inference time, and resource requirements, especially for large-scale deployment.",
"explanation": "Understanding computational demands is essential for practical implementation and scalability of the models.",
"location": "Technical analysis",
"category": "complexity",
"focus": "complexity"
},
{
"original_text": "The hyperparameter grid details are only referenced as in Appendix 6.1 without main text elaboration.",
"improved_version": "Summarize key hyperparameter ranges and tuning procedures within the main manuscript to improve transparency and facilitate replication.",
"explanation": "Main text summaries aid comprehension and ensure critical methodological details are accessible without consulting appendices.",
"location": "Methodology, hyperparameter tuning",
"category": "documentation",
"focus": "clarity"
},
{
"original_text": "The operational definitions of 'churned' and 'churning' users are based on last login dates, but potential ambiguities at program end are not discussed.",
"improved_version": "Clarify how the model distinguishes between users who stop using the app due to program completion versus disengagement, possibly by incorporating program end dates or additional engagement metrics.",
"explanation": "This clarification reduces misclassification and improves the validity of churn definitions in the context of program end versus disengagement.",
"location": "Discussion, limitations",
"category": "edge_cases",
"focus": "edge_cases"
},
{
"original_text": "The models are primarily evaluated using metrics like AUC, F1, accuracy, etc., but the potential impact of false positives/negatives on clinical decision-making is not discussed.",
"improved_version": "Include an analysis of the clinical or operational implications of false positive and false negative predictions, possibly with threshold adjustments for optimal trade-offs.",
"explanation": "Understanding the practical impact of prediction errors informs deployment strategies and risk management in real-world settings.",
"location": "Discussion, implications",
"category": "technical_documentation",
"focus": "clarity"
}
],
"detailed_feedback": {
"derivation_correctness": "The paper mentions normalization techniques like square root scaling and standard scaling but does not provide explicit formulas or mathematical derivations. Including these would clarify the preprocessing steps and facilitate exact replication. For example, specifying that 'for right-skewed features, x' = sqrt(x + 1)' was applied, and that standard scaling involved subtracting the mean and dividing by the standard deviation, would improve transparency.",
"algorithm_accuracy": "The use of random forest classifiers is justified with references to prior literature, and hyperparameter tuning via grid search and cross-validation is described. However, details such as the specific hyperparameter ranges, number of trees, maximum depth, and feature subset sizes are not provided. Including these would strengthen confidence in the model's correctness and allow others to replicate or evaluate the models' efficiency.",
"terminology_accuracy": "The paper distinguishes between 'nonadherence,' 'churn,' and 'disengagement,' but the operational definitions vary slightly across contexts. Clarifying that 'nonadherence' refers to not meeting the prescribed activity threshold, while 'churn' indicates complete discontinuation, and ensuring consistent use throughout, would improve terminological precision.",
"equation_clarity": "Explicit formulas for feature transformations, such as normalization, are absent. Including equations like 'x' = sqrt(x + 1) for skewed features or 'x' = (x - \u03bc) / \u03c3 for standard scaling, would enhance clarity and reproducibility.",
"content_completeness": "While performance metrics are comprehensively reported, the methodology for handling class imbalance (e.g., undersampling with Tomek Links) is only briefly mentioned. Providing detailed procedures, including the parameters and rationale, would improve completeness and allow critical assessment.",
"logical_consistency": "The paper maintains consistent definitions of prediction windows and aligns the operationalization of adherence and churn with prior literature. However, the varying thresholds for adherence (e.g., 8 exercises/week vs 1 blood pressure measurement/month) could be unified or justified more explicitly to ensure logical coherence across different conditions.",
"implementation_details": "The mention of Python packages and hyperparameter grids is limited to appendix references. Including key package versions, code snippets, or pseudocode in the main text would enhance transparency and facilitate independent validation.",
"edge_case_handling": "The models assume continuous, rich engagement data, but the paper does not explicitly address how sparse or irregular data are handled, especially for users with very short engagement periods or missing data. Discussing strategies like imputation, data augmentation, or model adjustments for such cases would improve robustness.",
"complexity_analysis": "The manuscript does not discuss the computational complexity or resource requirements of the random forest models. Including an analysis of training and inference times, especially for large datasets, would inform practical deployment considerations.",
"technical_documentation": "Hyperparameter tuning procedures, feature importance analyses, and evaluation metrics are referenced but not detailed in the main text. Providing summarized tables or descriptions of these processes would improve transparency and reproducibility."
},
"summary": "Overall, the manuscript demonstrates a strong understanding of machine learning applications in mHealth adherence prediction, with robust performance metrics and thoughtful analysis. However, enhancing transparency in preprocessing formulas, hyperparameter details, and handling of edge cases would elevate the technical rigor. Addressing these areas will facilitate replication, practical deployment, and broader applicability of the proposed models."
}

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@@ -1,186 +0,0 @@
{
"consistency_score": 7,
"critical_remarks": [
{
"category": "methods_results",
"location": "Section 2.2, paragraph starting with 'Aligning with previous churn prediction studies' ",
"issue": "The description of feature selection emphasizes behavioral engagement features but lacks clarity on how these features directly relate to the specific nonadherence definitions used in the results, especially for the weekly and monthly measures.",
"severity": "medium",
"impact": "This ambiguity may cause confusion about whether the features used are fully aligned with the operational definitions of nonadherence and churn, affecting the interpretability of the results."
},
{
"category": "results_conclusions",
"location": "Section 3.2, paragraph starting 'Weekly nonadherence prediction models in Vivira demonstrated strong performances' ",
"issue": "The results show high performance metrics, but the discussion does not sufficiently address the potential impact of class imbalance on these metrics, especially since the adherence rates decline sharply over time.",
"severity": "high",
"impact": "This omission could lead to overestimating the models' robustness and their practical utility in real-world, highly imbalanced settings."
},
{
"category": "logical_flow",
"location": "Section 4, paragraph starting 'Our findings show that nonadherence can be accurately predicted' ",
"issue": "The paragraph jumps from discussing model performance to implications for targeted strategies without clearly linking how the predictive accuracy translates into actionable interventions.",
"severity": "medium",
"impact": "This weakens the logical progression from results to practical applications, reducing clarity of the study's translational significance."
},
{
"category": "terminology",
"location": "Throughout the manuscript, especially in Abstract and Introduction",
"issue": "The terms 'nonadherence', 'churn', and 'disengagement' are used somewhat interchangeably without explicit definitions or distinctions in some contexts.",
"severity": "low",
"impact": "Inconsistent terminology may cause confusion about what exactly is being predicted and how different concepts relate, affecting clarity."
},
{
"category": "hypothesis",
"location": "Section 2, Introduction",
"issue": "The hypothesis that behavioral app engagement features can predict nonadherence over extended periods is implied but not explicitly stated or tested as a formal hypothesis.",
"severity": "low",
"impact": "Explicit hypotheses would strengthen the scientific framing and clarity of the research aims."
},
{
"category": "interpretation",
"location": "Section 4, Discussion",
"issue": "The interpretation that models 'outperform' in early weeks without considering the influence of class imbalance or baseline rates may be overstated.",
"severity": "medium",
"impact": "This could lead to overconfidence in the models' early predictive utility, potentially misleading readers about their practical deployment."
},
{
"category": "citations",
"location": "Throughout the manuscript, especially in the Introduction and Discussion",
"issue": "Some citations (e.g., [21], [5]) are used to support claims about prior work but are not always directly linked to the specific points made, and some references (e.g., [50]) are not consistently integrated with the discussion of adherence definitions.",
"severity": "low",
"impact": "Inconsistent citation integration may weaken the scholarly rigor and traceability of claims."
},
{
"category": "figures",
"location": "Section 3, Figures 6.5 and 6.6",
"issue": "The figure captions describe performance trends but do not explicitly connect the visual data to the text's claims about model improvements over time or differences between user groups.",
"severity": "low",
"impact": "This reduces clarity and interpretability of the figures, limiting their effectiveness as supporting evidence."
},
{
"category": "tables",
"location": "Section 3, Tables 6.1 to 6.4",
"issue": "Some tables present extensive data, but the alignment between the table content and the narrative summaries is sometimes weak, e.g., performance metrics are discussed in the text but not always explicitly linked to specific table rows or columns.",
"severity": "medium",
"impact": "This can cause confusion and reduce the ease of cross-referencing results, affecting overall coherence."
},
{
"category": "supplementary",
"location": "Appendix 6.2",
"issue": "The appendix contains feature importance analyses, but the main text does not sufficiently highlight how these findings support the claims about feature relevance and model robustness.",
"severity": "low",
"impact": "Limited integration diminishes the clarity of how supplementary analyses underpin the main conclusions."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "The rich behavioral engagement data collected by mHealth interventions form the basis for predicting nonadherence, and explicitly testing this relationship is a core aim of this study.",
"explanation": "Clarifies the link between data and the research aim, strengthening the methodological rationale.",
"location": "Abstract",
"category": "abstract",
"focus": "methods_results"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.",
"improved_version": "Our models correctly identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira, where nonadherence was operationalized as completing fewer than eight exercises per week, with a mean AUC of 0.95.",
"explanation": "Ensures clarity by explicitly linking the performance metrics with the operational definition of nonadherence, improving transparency.",
"location": "Abstract",
"category": "abstract",
"focus": "results_conclusions"
},
{
"original_text": "The description of feature selection emphasizes behavioral engagement features but lacks clarity on how these features directly relate to the specific nonadherence definitions used in the results.",
"improved_version": "Feature selection focused on behavioral engagement metrics such as daily app activity, session counts, and exercise completion, which directly correspond to the operational definitions of nonadherence (e.g., weekly exercise thresholds).",
"explanation": "Aligns features explicitly with the operational definitions, enhancing methodological transparency.",
"location": "Section 2.2",
"category": "methodology",
"focus": "methods_results"
},
{
"original_text": "The results show high performance metrics, but the discussion does not sufficiently address the potential impact of class imbalance on these metrics.",
"improved_version": "While the models demonstrate high performance, it is important to consider that class imbalance\u2014particularly in adherence rates\u2014may inflate metrics like accuracy and F1-score; thus, metrics such as precision-recall curves should be interpreted with caution.",
"explanation": "Provides a nuanced interpretation, acknowledging limitations and preventing overstatement of results.",
"location": "Section 3.2",
"category": "results_conclusions",
"focus": "interpretation"
},
{
"original_text": "The paragraph jumps from discussing model performance to implications for targeted strategies without clearly linking how the predictive accuracy translates into actionable interventions.",
"improved_version": "The high predictive accuracy suggests that these models could be integrated into intervention frameworks to identify at-risk users early, enabling timely, targeted strategies such as personalized notifications or system adaptations to prevent further disengagement.",
"explanation": "Strengthens the logical link between results and practical applications, clarifying translational relevance.",
"location": "Section 4, Discussion",
"category": "logical_flow",
"focus": "logical_flow"
},
{
"original_text": "The terms 'nonadherence', 'churn', and 'disengagement' are used somewhat interchangeably without explicit definitions or distinctions.",
"improved_version": "Throughout the manuscript, 'nonadherence' refers to users not meeting the recommended engagement thresholds, while 'churn' specifically denotes complete discontinuation of app use, and 'disengagement' is used as a broader term encompassing both phenomena. Clear distinctions are maintained to avoid confusion.",
"explanation": "Clarifies terminology, improving consistency and reader understanding.",
"location": "Throughout",
"category": "terminology",
"focus": "terminology"
},
{
"original_text": "The hypothesis that behavioral app engagement features can predict nonadherence over extended periods is implied but not explicitly stated or tested as a formal hypothesis.",
"improved_version": "This study hypothesizes that behavioral app engagement features, such as daily activity and exercise completion, can accurately predict nonadherence and churn over extended durations in mHealth interventions.",
"explanation": "Explicitly states the hypothesis, strengthening the scientific framing.",
"location": "Section 2, Introduction",
"category": "hypothesis",
"focus": "hypothesis"
},
{
"original_text": "The interpretation that models 'outperform' in early weeks without considering the influence of class imbalance or baseline rates may be overstated.",
"improved_version": "Although early-week performance metrics are promising, they should be interpreted cautiously, considering the potential influence of class imbalance and baseline adherence rates, which may inflate performance indicators.",
"explanation": "Provides a balanced interpretation, acknowledging potential confounders.",
"location": "Section 4, Discussion",
"category": "interpretation",
"focus": "interpretation"
},
{
"original_text": "Some citations (e.g., [21], [5]) are used to support claims about prior work but are not always directly linked to the specific points made.",
"improved_version": "Citations such as [21] and [5] are explicitly linked to prior studies on early user churn prediction, providing direct support for the claims about methodological foundations and performance benchmarks.",
"explanation": "Enhances scholarly rigor by ensuring citations directly support specific statements.",
"location": "Throughout",
"category": "citations",
"focus": "citations"
},
{
"original_text": "The figure captions describe performance trends but do not explicitly connect the visual data to the text's claims about model improvements over time.",
"improved_version": "Figure 6.5 illustrates the steady increase in AUC and F1-score over Weeks 2 to 13, supporting the text's claim of improving model performance with accumulating data.",
"explanation": "Explicitly links figures to narrative claims, improving interpretability.",
"location": "Section 3",
"category": "figures",
"focus": "figures"
},
{
"original_text": "Some tables present extensive data, but the alignment between the table content and the narrative summaries is sometimes weak.",
"improved_version": "Ensure that each key performance metric discussed in the text is directly cross-referenced with the corresponding table rows and columns, e.g., 'as shown in Table 6.1, the AUC increased from 0.89 in Week 2 to 0.99 in Week 13.'",
"explanation": "Facilitates easier cross-referencing and comprehension, enhancing coherence.",
"location": "Section 3",
"category": "tables",
"focus": "tables"
},
{
"original_text": "The appendix contains feature importance analyses, but the main text does not sufficiently highlight how these findings support the claims about feature relevance and model robustness.",
"improved_version": "The main text references Appendix 6.2 to support claims that exercise-related features are most influential, emphasizing the robustness of behavioral features in predicting nonadherence across time.",
"explanation": "Strengthens the link between supplementary analyses and main conclusions, improving coherence.",
"location": "Section 4, Discussion",
"category": "supplementary",
"focus": "supplementary"
}
],
"detailed_feedback": {
"methods_results_alignment": "The methodology section details the feature selection and modeling approach, focusing on behavioral engagement metrics like daily activity and exercise completion. The results demonstrate high predictive performance for nonadherence and churn, aligning well with these methods. However, explicit discussion on how these features directly operationalize the adherence definitions would improve clarity.",
"results_conclusions_alignment": "The results show strong predictive metrics, supporting the conclusion that nonadherence can be predicted over extended periods. Nonetheless, the discussion should more explicitly address how the performance metrics translate into real-world intervention potential, considering class imbalance and model limitations.",
"logical_flow": "The manuscript generally follows a logical sequence from background, methods, results, to discussion. Yet, transitions between model performance results and their practical implications could be smoother, explicitly linking predictive accuracy to intervention strategies.",
"terminology_consistency": "The manuscript uses 'nonadherence', 'churn', and 'disengagement' with some overlap but inconsistent distinctions. Clarifying and consistently applying these terms throughout would enhance clarity.",
"hypothesis_testing": "While the introduction implies that behavioral engagement features can predict nonadherence, the manuscript would benefit from explicitly stating and testing this as a formal hypothesis to strengthen scientific rigor.",
"interpretation_consistency": "The interpretation of high performance metrics, especially early in the program, should consider potential confounders like class imbalance. Overstating early predictive utility without this context could mislead readers.",
"citation_consistency": "Some references support broad claims but are not always directly linked to the specific points. More precise citation integration would improve scholarly rigor.",
"figure_text_alignment": "Figures effectively illustrate performance trends but lack explicit references in the captions to how they support the narrative claims. Enhancing this connection would improve interpretability.",
"table_text_alignment": "Tables contain comprehensive data, but the narrative sometimes lacks direct cross-referencing. Clearer linkage between text and table data would improve coherence.",
"supplementary_consistency": "Appendix analyses support main findings but are under-referenced in the text. Explicitly discussing how these analyses underpin key conclusions would strengthen the overall coherence."
},
"summary": "Overall, the manuscript demonstrates a solid integration of methods and results, with clear evidence supporting the claim that behavioral app engagement features can predict nonadherence over extended periods. However, improvements in terminology clarity, explicit hypothesis framing, and stronger linking of figures, tables, and supplementary analyses to the main narrative would elevate the logical coherence and scholarly rigor. Addressing these aspects will enhance the clarity, transparency, and practical relevance of the research findings."
}

181
Backup/V5_multi_agent2/results/S10_results.json
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@@ -1,181 +0,0 @@
{
"score": 8,
"critical_remarks": [
{
"category": "completeness",
"location": "Section 2.2, Feature Selection, Model Training, and Evaluation",
"issue": "While hyperparameter grids are mentioned to be in Appendix 6.1, the main text lacks a summary of key hyperparameters and their ranges, which would aid understanding without requiring immediate appendix access.",
"severity": "medium",
"impact": "This reduces transparency about model tuning and may hinder reproducibility or critical assessment of the modeling process."
},
{
"category": "relevance",
"location": "Section 3.2.1, Vivira prediction results",
"issue": "The detailed tables and figures, while comprehensive, focus heavily on statistical metrics without explicitly linking how these translate into practical intervention strategies or clinical relevance.",
"severity": "medium",
"impact": "This limits the reader's ability to assess real-world applicability of the predictive performance."
},
{
"category": "clarity",
"location": "Section 4.1, Summary of prediction results",
"issue": "The description of performance metrics over time is dense and could benefit from clearer summaries or visualizations to facilitate quick understanding.",
"severity": "low",
"impact": "This affects accessibility for readers seeking a quick grasp of model effectiveness."
},
{
"category": "organization",
"location": "Section 4.2, Potential for targeted strategies",
"issue": "The discussion on reengagement after correct predictions is somewhat scattered, with multiple percentages and user categories presented without a clear structure.",
"severity": "low",
"impact": "This hampers the logical flow and makes it harder for readers to follow key insights."
},
{
"category": "completeness",
"location": "Section 4.4, Limitations and future work",
"issue": "While limitations are acknowledged, there is limited discussion on potential biases introduced by the datasets or the impact of demographic variables on model performance.",
"severity": "medium",
"impact": "This omission affects the thoroughness of the critical evaluation and the generalizability of findings."
},
{
"category": "clarity",
"location": "Appendix 1, Software packages and hyperparameter grids",
"issue": "The hyperparameter grids are only referenced as being in Appendix 6.1, but no summary or key examples are provided in the main text, which could aid understanding of model complexity.",
"severity": "low",
"impact": "This reduces transparency and may hinder replication or critical appraisal."
},
{
"category": "relevance",
"location": "Section 4.3, Implications for researchers and providers",
"issue": "The discussion on expanding feature sets to include intervention-specific attributes is somewhat generic; specific examples tailored to the interventions studied could enhance relevance.",
"severity": "low",
"impact": "This would improve the practical applicability of recommendations."
},
{
"category": "completeness",
"location": "Section 4.4, Limitations",
"issue": "The potential impact of data quality issues, such as missing data or measurement errors, is not discussed.",
"severity": "medium",
"impact": "This omission limits understanding of data robustness and model reliability."
},
{
"category": "organization",
"location": "Overall structure",
"issue": "The supplementary materials include extensive tables and figures, but their integration into the narrative could be improved with more explicit cross-referencing and summaries.",
"severity": "low",
"impact": "This affects navigability and ease of extracting key insights."
},
{
"category": "clarity",
"location": "Section 3.2.2, Manoa prediction results",
"issue": "The description of performance trends over months is detailed but could benefit from simplified summaries or visual aids for clarity.",
"severity": "low",
"impact": "This would enhance accessibility for diverse audiences."
}
],
"improvement_suggestions": [
{
"original_text": "Detailed hyperparameter grids are provided in Appendix 6.1.",
"improved_version": "Include a summary table or key hyperparameter ranges within the main text to clarify the model tuning process.",
"explanation": "Providing a concise overview enhances transparency and helps readers understand the model complexity without immediately consulting the appendix.",
"location": "Section 2.2, Appendix 6.1",
"category": "completeness",
"focus": "detail"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13.",
"improved_version": "Summarize the key performance metrics (e.g., AUC, F1) in a visual format like a line graph to illustrate trends over weeks.",
"explanation": "Visual summaries improve clarity and allow quick assessment of model performance evolution over time.",
"location": "Section 3.2.1",
"category": "clarity",
"focus": "presentation"
},
{
"original_text": "The discussion on reengagement after correct predictions is somewhat scattered.",
"improved_version": "Reorganize this section into a clear subsection with bullet points or numbered lists highlighting the main findings: proportions reengaging, implications, and potential strategies.",
"explanation": "Structured presentation enhances readability and emphasizes key insights for practical application.",
"location": "Section 4.2",
"category": "organization",
"focus": "structure"
},
{
"original_text": "While the datasets are described, the potential biases due to non-consenting users are not discussed.",
"improved_version": "Add a paragraph discussing possible selection bias introduced by excluding non-consenting users and how this might affect generalizability.",
"explanation": "Addressing dataset biases improves the thoroughness of the limitations section and informs interpretation of results.",
"location": "Section 4.4",
"category": "completeness",
"focus": "thoroughness"
},
{
"original_text": "The performance metrics are dense and could be summarized more clearly.",
"improved_version": "Create a summary table that highlights the key performance metrics (AUC, accuracy, F1) across different weeks/months for each model, with color coding for performance levels.",
"explanation": "This makes complex data more accessible and facilitates comparison across models and time points.",
"location": "Section 3.2.1, 3.2.2",
"category": "clarity",
"focus": "presentation"
},
{
"original_text": "The discussion on feature importance analyses is brief and references appendices.",
"improved_version": "Include a brief summary of the main findings from the feature importance analyses within the main text, highlighting which features were most predictive and how their importance changed over time.",
"explanation": "This enhances understanding of model interpretability and practical relevance without requiring appendix consultation.",
"location": "Section 4.1",
"category": "completeness",
"focus": "detail"
},
{
"original_text": "The supplementary materials include extensive tables and figures, but their integration could be improved.",
"improved_version": "Add more explicit cross-references in the main text to specific tables and figures, and include brief interpretive captions or summaries for each visual aid.",
"explanation": "Improved linking and captions facilitate navigation and comprehension for readers reviewing supplementary data.",
"location": "Throughout",
"category": "organization",
"focus": "accessibility"
},
{
"original_text": "The predictive utility during early days is limited, but this is not deeply discussed.",
"improved_version": "Expand the discussion to include potential strategies for improving early-day predictions, such as incorporating additional data sources or adjusting prediction windows.",
"explanation": "Addressing this limitation explicitly guides future research and model development efforts.",
"location": "Section 4.4",
"category": "completeness",
"focus": "thoroughness"
},
{
"original_text": "The link between adherence and health outcomes is briefly mentioned as uncertain.",
"improved_version": "Include references to ongoing or planned studies that aim to evaluate the impact of adherence prediction and intervention on actual health outcomes, emphasizing the importance of this future work.",
"explanation": "This contextualizes the significance of the predictive models within broader health impact goals and highlights research gaps.",
"location": "Section 4.4",
"category": "relevance",
"focus": "connection"
},
{
"original_text": "The discussion on intervention-specific features is generic.",
"improved_version": "Provide concrete examples tailored to the studied interventions, such as including features like 'number of exercise videos watched' for Vivira or 'number of blood pressure readings' for Manoa, and discuss how these could enhance model performance.",
"explanation": "Specific examples increase practical relevance and guide future feature engineering efforts.",
"location": "Section 4.3",
"category": "relevance",
"focus": "connection"
},
{
"original_text": "The limitations section does not address potential biases from demographic variables.",
"improved_version": "Add a paragraph discussing how demographic factors like age, gender, or health status might influence model performance and the importance of stratified analyses or bias mitigation strategies.",
"explanation": "This enhances the thoroughness of limitations and informs future model fairness assessments.",
"location": "Section 4.4",
"category": "completeness",
"focus": "thoroughness"
},
{
"original_text": "The supplementary materials are extensive but could benefit from a summary table of key findings.",
"improved_version": "Include a summary table at the end of the supplementary section that consolidates main performance metrics, sample sizes, and key insights for quick reference.",
"explanation": "This improves usability and allows readers to grasp core results without parsing all detailed data.",
"location": "Appendix",
"category": "organization",
"focus": "accessibility"
}
],
"detailed_feedback": {
"relevance_analysis": "The supplementary materials are highly relevant to the main text, providing detailed data, tables, and figures that support the reported predictive performance and methodological approaches. They enhance transparency and allow for in-depth evaluation of the models and datasets, aligning well with the research objectives of predicting nonadherence and churn in mHealth interventions.",
"clarity_analysis": "While the materials are comprehensive, the presentation of complex data\u2014especially performance metrics over multiple weeks and months\u2014can be overwhelming. Incorporating visual summaries, clearer headings, and simplified language in some sections would improve accessibility and facilitate quicker understanding for diverse audiences.",
"consistency_analysis": "The supplementary data consistently align with the descriptions in the main text, with tables and figures matching the reported results. Cross-references are generally clear, although more explicit linking between figures, tables, and narrative explanations would reinforce coherence.",
"completeness_analysis": "The materials are detailed, including extensive tables, descriptive statistics, and performance metrics. However, some methodological details\u2014such as hyperparameter choices, data quality considerations, and potential biases\u2014are only briefly mentioned or relegated to appendices, which could be expanded for full transparency.",
"organization_analysis": "The supplementary files are logically structured into sections covering datasets, results, and appendices. Nonetheless, the integration could be improved by adding summary tables, clearer cross-references, and more consistent formatting to guide the reader through the extensive data efficiently."
},
"summary": "Overall, the supplementary materials are of high quality, providing comprehensive and detailed data that support the main manuscript. The main areas for improvement involve enhancing clarity through visual summaries, explicitly linking data to practical implications, and expanding methodological transparency. Addressing these points would elevate the usability, interpretability, and impact of the supplementary content, making it more accessible and informative for a broad scientific audience."
}

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Backup/V5_multi_agent2/results/S1_results.json
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@@ -1,65 +0,0 @@
{
"title_keywords_score": 7,
"critical_remarks": [
{
"category": "title_clarity",
"location": "Title",
"issue": "The current title clearly indicates the focus on predicting nonadherence in mobile health interventions but could benefit from more specificity regarding the methodology and scope.",
"severity": "medium",
"impact": "While understandable, the title may not fully convey the innovative aspect of the predictive modeling approach or the extended duration, potentially limiting immediate clarity for targeted audiences."
},
{
"category": "title_length",
"location": "Title",
"issue": "The title is somewhat lengthy, which may reduce its impact and ease of discoverability in search results.",
"severity": "low",
"impact": "A more concise title could improve readability and search engine optimization without sacrificing essential information."
},
{
"category": "keywords_relevance",
"location": "Keywords",
"issue": "No keywords section is explicitly present in the manuscript.",
"severity": "high",
"impact": "Lack of keywords hampers discoverability and indexing, reducing the manuscript\u2019s visibility in relevant searches."
},
{
"category": "keywords_coverage",
"location": "Keywords",
"issue": "No keywords section is found; therefore, coverage cannot be assessed.",
"severity": "high",
"impact": "Absence of keywords limits the manuscript\u2019s reach to interested researchers and practitioners searching for related topics."
},
{
"category": "guidelines",
"location": "Title",
"issue": "The title adheres to standard conventions in academic publishing, clearly stating the research focus without jargon.",
"severity": "low",
"impact": "Supports proper indexing and comprehension by the target academic audience."
},
{
"category": "discoverability",
"location": "Title",
"issue": "The current title includes relevant keywords but could be optimized for SEO by incorporating more specific terms related to methodology and outcomes.",
"severity": "medium",
"impact": "Enhanced discoverability in digital searches, especially for keywords like 'machine learning,' 'predicting nonadherence,' and 'extended duration,' could attract a broader audience."
}
],
"improvement_suggestions": [
{
"original_text": "Predicting Nonadherence to Mobile Health Interventions",
"improved_version": "Machine Learning-Based Prediction of Nonadherence in Extended Mobile Health Interventions for Chronic Disease Management",
"explanation": "This revised title enhances clarity by specifying the use of machine learning, emphasizes the focus on nonadherence prediction, and highlights the extended duration of interventions. It balances impact and SEO by including relevant keywords such as 'machine learning,' 'prediction,' 'nonadherence,' 'mobile health,' and 'chronic disease management,' aligning with field standards for descriptive and discoverable titles.",
"location": "Title",
"category": "title",
"focus": "comprehensive_improvement"
}
],
"detailed_feedback": {
"title_analysis": "The current title effectively communicates the core topic of predicting nonadherence in mobile health interventions, maintaining clarity and adherence to academic standards. However, it could be more impactful by including methodological keywords and scope details to improve searchability and audience engagement.",
"keywords_analysis": "No keywords section found",
"guidelines_compliance": "The title follows standard academic conventions, being concise and descriptive without jargon. It clearly states the research focus, aligning with typical scholarly standards for clarity and relevance.",
"discoverability_assessment": "The title contains relevant keywords but lacks optimization for search engines. Incorporating specific terms like 'machine learning,' 'chronic disease,' and 'extended duration' would improve visibility in digital searches and indexing, facilitating easier discovery by interested researchers.",
"audience_alignment": "The title appeals to researchers and practitioners interested in digital health, machine learning, and adherence prediction. Its clarity and specificity align with the audience's expectations for impactful, relevant research in the field."
},
"summary": "Overall, the manuscript's title is clear and standards-compliant but can be significantly improved for discoverability and impact by including specific methodological and scope-related keywords. The absence of a dedicated keywords section limits searchability, which should be addressed to enhance the manuscript's visibility and reach within the academic community."
}

138
Backup/V5_multi_agent2/results/S2_results.json
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@@ -1,138 +0,0 @@
{
"score": 6,
"critical_remarks": [
{
"category": "structure",
"location": "Abstract",
"issue": "The abstract combines multiple complex results and methodological details without clear section demarcation, making it difficult to follow the logical flow of background, methods, results, and conclusions.",
"severity": "high",
"impact": "Reduces clarity and hampers quick comprehension of the study's purpose, approach, and key findings, which is essential for scientific communication."
},
{
"category": "content",
"location": "Introduction and Methods",
"issue": "The abstract lacks explicit mention of the specific machine learning algorithms used, the rationale for their selection, and the detailed validation procedures, limiting understanding of methodological rigor.",
"severity": "high",
"impact": "Impairs assessment of technical validity and reproducibility of the predictive models, which are central to the study's contribution."
},
{
"category": "clarity",
"location": "Results and Discussion",
"issue": "The dense presentation of performance metrics (AUC, accuracy, F1, etc.) across multiple weeks and models is overwhelming and poorly summarized, reducing readability.",
"severity": "medium",
"impact": "Hinders quick grasp of key findings and their implications, especially for readers less familiar with statistical metrics."
},
{
"category": "standards",
"location": "Introduction and Methods",
"issue": "The abstract and main text do not consistently follow standard scientific reporting conventions, such as clearly separating background, methods, results, and discussion, and providing sufficient methodological detail.",
"severity": "medium",
"impact": "Weakens scientific rigor and reproducibility, which are crucial for peer evaluation and future research."
},
{
"category": "impact",
"location": "Discussion",
"issue": "While the abstract emphasizes the predictive accuracy, it underrepresents the potential limitations and real-world applicability challenges, such as data sparsity or generalizability to broader populations.",
"severity": "medium",
"impact": "Limits the perceived significance and practical utility of the findings, reducing the manuscript's overall impact."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "This study investigates whether objective behavioral engagement data from mHealth interventions can accurately predict nonadherence and user churn over extended periods.",
"explanation": "Clarifies the research question and emphasizes the focus on predictive capability, improving clarity and framing.",
"location": "Abstract",
"category": "clarity",
"focus": "organization"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.",
"improved_version": "In Vivira, the models achieved an average recall of 94% in identifying nonadherent users (defined as completing fewer than 8 exercises per week) between Weeks 2 and 13, with a mean AUC of 0.95.",
"explanation": "Rephrases for clarity, explicitly linking the metric (recall) with the definition of nonadherence and highlighting model performance succinctly.",
"location": "Abstract",
"category": "clarity",
"focus": "results"
},
{
"original_text": "The study also analyzed the number of users who reengage after a correct churn prediction, offering insights into the potential for targeted strategies to promote adherence.",
"improved_version": "Additionally, the study examined re-engagement rates following correct churn predictions, providing insights into how predictive models could inform targeted adherence-promoting interventions.",
"explanation": "Improves readability and emphasizes the practical relevance of re-engagement analysis for intervention strategies.",
"location": "Abstract",
"category": "clarity",
"focus": "results"
},
{
"original_text": "The introduction provides extensive background but could benefit from a clearer statement of the study\u2019s specific aims and hypotheses.",
"improved_version": "The primary aim of this study is to evaluate the predictive accuracy of machine learning models in identifying nonadherence and user churn in two distinct mHealth interventions over extended durations, with the hypothesis that behavioral app engagement features can reliably forecast future nonadherence.",
"explanation": "Adds clarity by explicitly stating the aims and hypotheses, aligning background with research objectives.",
"location": "Introduction",
"category": "clarity",
"focus": "organization"
},
{
"original_text": "We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning on the training sets, optimizing for F1 score for all models.",
"improved_version": "Model training involved stratified 10-fold cross-validation combined with randomized hyperparameter search, with the optimization criterion set to maximize the F1 score, ensuring balanced performance across classes.",
"explanation": "Clarifies the methodological approach and rationale, improving technical transparency.",
"location": "Methods",
"category": "methodology",
"focus": "methodology"
},
{
"original_text": "The performance metrics showed a gradual improvement over time, but early predictions (Days 1-3) had limited utility.",
"improved_version": "Performance metrics indicated progressive improvement over the observation period, although predictions within the first three days showed limited accuracy, likely due to class imbalance and sparse early engagement data.",
"explanation": "Provides a more nuanced explanation of early prediction limitations, enhancing interpretability.",
"location": "Results",
"category": "results",
"focus": "results"
},
{
"original_text": "The discussion emphasizes the predictive accuracy but should more thoroughly address limitations such as data sparsity, generalizability, and potential biases.",
"improved_version": "While the models demonstrate high predictive accuracy, limitations include potential biases due to the specific populations studied, the reliance on continuous engagement data, and challenges in generalizing to interventions with lower retention rates or different engagement patterns.",
"explanation": "Provides a balanced discussion of limitations, improving scientific rigor and transparency.",
"location": "Discussion",
"category": "standards",
"focus": "impact"
},
{
"original_text": "The abstract and main text could better separate background, methods, results, and conclusions for clearer communication.",
"improved_version": "Reorganize the abstract into distinct sections: background (brief context), methods (study design, data, modeling approach), results (performance metrics), and conclusions (implications and future directions).",
"explanation": "Enhances clarity and adherence to standard scientific reporting formats.",
"location": "Abstract",
"category": "standards",
"focus": "organization"
},
{
"original_text": "The models' performance metrics are presented in detail, but a summary table or figure highlighting key results across weeks would improve readability.",
"improved_version": "Include a summary table or figure that consolidates key performance metrics (AUC, accuracy, F1) across all prediction weeks, facilitating quick comparison and interpretation.",
"explanation": "Improves readability and allows readers to grasp performance trends at a glance.",
"location": "Results",
"category": "clarity",
"focus": "readability"
},
{
"original_text": "The discussion should better connect the findings to practical implementation and potential challenges in real-world settings.",
"improved_version": "Strengthen the discussion by explicitly addressing how these predictive models could be integrated into clinical workflows, potential barriers such as data privacy, user acceptance, and the need for prospective validation studies.",
"explanation": "Enhances impact by linking findings to real-world applicability and challenges.",
"location": "Discussion",
"category": "impact",
"focus": "impact"
},
{
"original_text": "The abstract contains extensive technical details that could be summarized more succinctly for broader accessibility.",
"improved_version": "Condense technical details by focusing on key performance metrics, main findings, and their implications, reserving detailed methodological descriptions for the main text.",
"explanation": "Improves readability and accessibility for a broader audience, including non-specialists.",
"location": "Abstract",
"category": "clarity",
"focus": "readability"
}
],
"detailed_feedback": {
"structure_analysis": "The abstract attempts to cover background, methods, results, and implications but lacks clear segmentation into these sections, leading to a dense, unstructured flow. The main text is comprehensive but could benefit from clearer organization, especially in separating background, aims, methods, results, and discussion points for better readability.",
"content_analysis": "The abstract and main text provide extensive data on model performance, datasets, and analysis, but some methodological details\u2014such as the specific ML algorithms, validation procedures, and hyperparameter tuning\u2014are insufficiently described. The results are rich but presented in a manner that overwhelms the reader, with performance metrics scattered throughout the text rather than summarized effectively.",
"clarity_assessment": "The language is technical and precise but often overly dense, especially in the results sections. The presentation of multiple metrics across many weeks without summarization hampers quick understanding. Simplifying language, adding summaries, and using visual aids like tables or figures would greatly improve readability.",
"standards_compliance": "The manuscript generally follows scientific standards in reporting results, but the abstract could be better structured into conventional sections. The detailed methodological descriptions are appropriate but could be more concise and clearer in explaining the ML approaches and validation strategies. The references are comprehensive and relevant.",
"impact_evaluation": "The study addresses a significant gap in predicting nonadherence over extended periods, which has high practical relevance. The findings suggest broad applicability of behavioral engagement features for predictive modeling, potentially informing targeted interventions. However, the manuscript could better articulate the real-world challenges and next steps for implementation, enhancing its overall impact."
},
"summary": "This manuscript presents a valuable and ambitious exploration of long-term nonadherence prediction in mHealth interventions using machine learning. While the technical rigor and extensive data are strengths, the overall clarity, structure, and presentation could be improved to make the findings more accessible and impactful. Addressing organizational issues, summarizing key results more effectively, and explicitly discussing practical implications and limitations would elevate the manuscript's quality and influence."
}

158
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@@ -1,158 +0,0 @@
{
"score": 7,
"critical_remarks": [
{
"category": "context",
"location": "Section 1 (Background and Context)",
"issue": "While the background discusses the importance of mHealth and NCDs, it lacks specific framing of the current challenges in adherence prediction and how existing gaps hinder progress.",
"severity": "medium",
"impact": "This limits the reader's understanding of the novelty and urgency of the research, reducing contextual clarity."
},
{
"category": "problem",
"location": "Section 1 (Problem Statement)",
"issue": "The problem of high nonadherence is mentioned broadly, but the introduction does not explicitly specify the limitations of current prediction methods or why existing models are insufficient for longer-term or nuanced predictions.",
"severity": "high",
"impact": "This weakens the justification for the study, making the problem seem less pressing or novel."
},
{
"category": "objectives",
"location": "Section 1 (Objectives Clarity)",
"issue": "The objectives are somewhat scattered; while the overall aim is to predict nonadherence, specific research questions or hypotheses are not clearly articulated upfront.",
"severity": "medium",
"impact": "This affects the clarity of the research scope and hampers the reader's ability to grasp precise aims."
},
{
"category": "significance",
"location": "Section 1 (Justification of Significance)",
"issue": "Although the potential of predictive models is discussed, the introduction does not sufficiently emphasize how this research advances current knowledge or impacts clinical practice.",
"severity": "medium",
"impact": "This diminishes the perceived importance and innovative contribution of the study."
},
{
"category": "literature integration",
"location": "Section 1 (Literature Review)",
"issue": "The review of prior studies is comprehensive but somewhat fragmented; it mentions many findings without synthesizing how these collectively inform the current research gap.",
"severity": "medium",
"impact": "This reduces coherence and makes it harder for readers to see the logical progression leading to the current study."
},
{
"category": "flow and organization",
"location": "Section 1 (Flow and Organization)",
"issue": "The transition from background to problem and then to objectives is somewhat abrupt; the narrative jumps between topics without smooth linking sentences.",
"severity": "low",
"impact": "This affects readability and the logical flow, potentially confusing readers."
},
{
"category": "technical accuracy",
"location": "Section 1 (Technical Content)",
"issue": "The definitions of adherence and churn are generally accurate but could benefit from clearer distinctions and more precise operationalization aligned with the study's focus.",
"severity": "low",
"impact": "This could lead to ambiguity in interpreting the results and their implications."
},
{
"category": "research scope",
"location": "Section 1 (Scope Clarification)",
"issue": "The scope is broad, covering two interventions and multiple prediction tasks, but the introduction does not explicitly delineate the boundaries or limitations of this scope.",
"severity": "low",
"impact": "This may cause overgeneralization or misinterpretation of the study's applicability."
},
{
"category": "hypotheses/questions",
"location": "Section 1 (Research Questions)",
"issue": "Explicit hypotheses or research questions are not clearly stated, which could guide the reader through the study's investigative focus.",
"severity": "medium",
"impact": "Lack of clear research questions diminishes the clarity of the study's aims and hinders focused evaluation."
}
],
"improvement_suggestions": [
{
"original_text": "The rising prevalence and economic burden of noncommunicable diseases (NCDs) present a significant challenge to patients and healthcare systems, calling for innovative, scalable, and cost-effective solutions.",
"improved_version": "Noncommunicable diseases (NCDs) are increasingly prevalent worldwide, imposing substantial health and economic burdens on patients and healthcare systems. This escalating challenge underscores the urgent need for innovative, scalable, and cost-effective interventions.",
"explanation": "Enhances clarity by emphasizing the global scope and urgency, setting a stronger foundation for the importance of the research.",
"location": "Section 1",
"category": "context",
"focus": "background"
},
{
"original_text": "Mobile health (mHealth) interventions, facilitated by the ubiquity of smartphones, have emerged as promising tools to support the prevention and management of NCDs.",
"improved_version": "The widespread adoption of smartphones has facilitated the development of mobile health (mHealth) interventions, which have shown promise in supporting the prevention and management of NCDs through accessible and scalable solutions.",
"explanation": "Provides a clearer link between smartphone ubiquity and the potential of mHealth, strengthening the background context.",
"location": "Section 1",
"category": "context",
"focus": "background"
},
{
"original_text": "Yet, despite growing evidence and availability, mHealth interventions face high nonadherence, where users fail to use these tools as intended or discontinue use entirely before achieving desired outcomes.",
"improved_version": "Despite the increasing evidence supporting their efficacy, a major challenge remains: high rates of nonadherence, where users either fail to follow prescribed usage patterns or discontinue use prematurely, thereby limiting the interventions' effectiveness.",
"explanation": "Clarifies the problem by specifying the nature of nonadherence and its impact on effectiveness.",
"location": "Section 1",
"category": "problem",
"focus": "problem"
},
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "The extensive behavioral data generated by mHealth interventions present an opportunity: can these data be leveraged to accurately predict nonadherence and enable timely interventions?",
"explanation": "Frames the problem as an opportunity, making the research gap more compelling and focused.",
"location": "Section 1",
"category": "problem",
"focus": "gap"
},
{
"original_text": "We developed machine learning models for the prediction of nonadherence in two mHealth interventions, one for nonspecific and degenerative back pain over a program duration of 90 days (Vivira, n = 8,372), and another for hypertension self-management over 186 days (Manoa, n = 6,674).",
"improved_version": "This study aims to develop and evaluate machine learning models to predict nonadherence in two distinct mHealth interventions: Vivira, a 90-day back pain program, and Manoa, a 186-day hypertension self-management app, to assess the generalizability and robustness of predictive approaches across different conditions and durations.",
"explanation": "Clarifies the objectives and scope, emphasizing the study's focus on model development and evaluation across interventions.",
"location": "Section 1",
"category": "objectives",
"focus": "objectives"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.",
"improved_version": "Our models achieved high predictive accuracy, correctly identifying an average of 94% of nonadherent users in Vivira during Weeks 2 to 13, where nonadherence was operationalized as completing fewer than eight exercises per week.",
"explanation": "Adds clarity to the operational definition of nonadherence and emphasizes model performance.",
"location": "Section 1",
"category": "objectives",
"focus": "objectives"
},
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "Given the richness of behavioral data from mHealth apps, this study investigates the extent to which nonadherence and churn can be accurately predicted over extended periods, enabling proactive intervention strategies.",
"explanation": "Aligns the research question with the data's potential, framing it as an investigation into predictive capacity.",
"location": "Section 1",
"category": "problem",
"focus": "gap"
},
{
"original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).",
"improved_version": "The study demonstrates that machine learning models can accurately predict nonadherence, including complete discontinuation (churn), over long-term intervention periods, highlighting the potential for early, targeted support.",
"explanation": "Strengthens the significance by linking prediction accuracy to practical intervention benefits.",
"location": "Section 4",
"category": "significance",
"focus": "impact"
},
{
"original_text": "Our descriptive analysis further emphasizes this relationship, showing that the decline in adherence over time in Vivira and Manoa is largely driven by churn (i.e., users discontinuing entirely).",
"improved_version": "Descriptive analyses reveal that the decline in adherence over time in both interventions is primarily attributable to user churn, underscoring the importance of early predictive detection.",
"explanation": "Clarifies the relationship and underscores the importance of early prediction for intervention planning.",
"location": "Section 4",
"category": "significance",
"focus": "impact"
},
{
"original_text": "The potential of predictive models is discussed, but the introduction does not sufficiently emphasize how this research advances current knowledge or impacts clinical practice.",
"improved_version": "This research advances current understanding by demonstrating the feasibility of long-term adherence prediction using objective behavioral data, paving the way for integrating predictive analytics into routine clinical workflows to improve patient engagement and outcomes.",
"explanation": "Highlights the contribution and practical relevance, strengthening the justification of the study.",
"location": "Section 4",
"category": "significance",
"focus": "impact"
}
],
"detailed_feedback": {
"context_analysis": "The introduction effectively outlines the global burden of NCDs and the promise of mHealth interventions, emphasizing their scalability and current adoption. However, it could better specify existing challenges in adherence prediction, especially over extended durations, and how current models fall short, to establish a clearer context for the study's necessity.",
"problem_analysis": "While high nonadherence rates are acknowledged, the introduction lacks a detailed critique of existing predictive approaches, their limitations in long-term or nuanced prediction, and the specific gaps this study aims to fill. Clarifying these points would strengthen the problem statement.",
"objectives_analysis": "The objectives are implied but not explicitly articulated as specific research questions or hypotheses. Clarifying whether the goal is to develop, validate, or compare models, and what specific aspects of nonadherence are targeted, would improve focus.",
"significance_assessment": "The potential impact on clinical practice and intervention strategies is mentioned but not deeply elaborated. Emphasizing how this research uniquely contributes to the field, advances predictive methodology, or influences health outcomes would enhance perceived importance.",
"structure_evaluation": "The introduction covers background, problem, and objectives but could benefit from clearer logical transitions. For example, explicitly linking the literature review to identified gaps and then to the study aims would improve flow and coherence."
},
"summary": "Overall, the introduction presents a relevant and timely topic with a solid foundation in existing literature. However, it would benefit from clearer articulation of the research gap, explicit research questions, and a stronger emphasis on the study's innovative contribution. Improving the logical flow and explicitly framing the objectives and significance would elevate the manuscript's clarity and impact."
}

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@@ -1,138 +0,0 @@
{
"score": 7,
"critical_remarks": [
{
"category": "coverage",
"location": "Section 1 & 2",
"issue": "While the review covers a broad range of mHealth applications and related adherence issues, it predominantly emphasizes quantitative prediction models without sufficiently addressing the diversity of intervention types, populations, or contextual factors influencing adherence.",
"severity": "medium",
"impact": "Limits the comprehensiveness of the review, potentially overlooking important contextual or qualitative factors affecting nonadherence."
},
{
"category": "analysis",
"location": "Section 4.1 & 4.4",
"issue": "The discussion on the limitations and future directions tends to be optimistic about model generalizability without critically examining potential biases, data quality issues, or the impact of unmeasured confounders.",
"severity": "high",
"impact": "Reduces the depth of critical evaluation, which is essential for understanding the robustness and applicability of the models."
},
{
"category": "structure",
"location": "Throughout the review",
"issue": "The organization of sections, especially the results and discussion, is dense with detailed data but lacks clear thematic segmentation or summaries that guide the reader through key insights.",
"severity": "medium",
"impact": "Impairs readability and hampers the synthesis of complex findings into digestible conclusions."
},
{
"category": "citations",
"location": "Section 2 & 4",
"issue": "While many references are relevant, there is a reliance on older or less recent studies for some foundational concepts, and some citations lack context or critical appraisal.",
"severity": "low",
"impact": "Slightly diminishes the currency and perceived rigor of the literature base."
},
{
"category": "integration",
"location": "Section 4.1 & 4.3",
"issue": "The review discusses models and features primarily in technical terms, with limited integration of how these models translate into real-world clinical or behavioral outcomes.",
"severity": "high",
"impact": "Weakens the connection between predictive modeling and practical intervention or health impact, reducing translational relevance."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "While the review highlights the potential of rich behavioral data from mHealth interventions, it would benefit from explicitly discussing the variability in data quality, completeness, and the influence of contextual factors on prediction accuracy.",
"explanation": "Adding nuance about data variability enhances understanding of the limitations and applicability of predictive models.",
"location": "Section 1",
"category": "coverage",
"focus": "breadth"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95),",
"improved_version": "Our models demonstrated high predictive accuracy, correctly identifying approximately 94% of nonadherent users in Vivira, though further discussion on potential false positives and model calibration is necessary to assess clinical utility.",
"explanation": "This clarifies the performance metrics and encourages critical evaluation of model limitations.",
"location": "Section 3.2.1",
"category": "analysis",
"focus": "synthesis"
},
{
"original_text": "The review predominantly emphasizes quantitative prediction models without sufficiently addressing the diversity of intervention types, populations, or contextual factors influencing adherence.",
"improved_version": "The review should incorporate a broader discussion of intervention diversity, including different health conditions, demographic groups, and settings, to better contextualize the applicability of the models.",
"explanation": "Expanding scope improves comprehensiveness and relevance across varied contexts.",
"location": "Section 2 & 4.4",
"category": "coverage",
"focus": "breadth"
},
{
"original_text": "While many references are relevant, there is a reliance on older or less recent studies for some foundational concepts, and some citations lack context or critical appraisal.",
"improved_version": "Update the reference list with more recent studies where available, and include brief critical evaluations of key citations to highlight their strengths and limitations.",
"explanation": "Enhances the currency and critical depth of the literature base.",
"location": "Section 2 & 4",
"category": "citations",
"focus": "relevance"
},
{
"original_text": "The organization of sections, especially the results and discussion, is dense with detailed data but lacks clear thematic segmentation or summaries that guide the reader through key insights.",
"improved_version": "Reorganize the results and discussion sections with clear subheadings and summary paragraphs that distill key findings and implications, improving readability and synthesis.",
"explanation": "Facilitates better comprehension and logical flow for readers.",
"location": "Section 3 & 4",
"category": "structure",
"focus": "organization"
},
{
"original_text": "The review discusses models and features primarily in technical terms, with limited integration of how these models translate into real-world clinical or behavioral outcomes.",
"improved_version": "Incorporate a dedicated discussion on how predictive models can inform clinical decision-making, behavioral interventions, and health outcomes, bridging the gap between technical performance and practical impact.",
"explanation": "Strengthens the translational relevance of the review.",
"location": "Section 4.3",
"category": "integration",
"focus": "relevance"
},
{
"original_text": "The discussion on the limitations and future directions tends to be optimistic about model generalizability without critically examining potential biases, data quality issues, or the impact of unmeasured confounders.",
"improved_version": "Expand the limitations section to critically evaluate potential biases, data quality concerns, and unmeasured confounders that may affect model validity and generalizability.",
"explanation": "Provides a more balanced and rigorous assessment of the models' robustness.",
"location": "Section 4.4",
"category": "analysis",
"focus": "depth"
},
{
"original_text": "The review could benefit from explicitly discussing the variability in data quality, completeness, and the influence of contextual factors on prediction accuracy.",
"improved_version": "Explicitly address how data quality, completeness, and contextual variables influence model performance and the generalizability of findings across different populations and settings.",
"explanation": "Enhances understanding of factors affecting model robustness and transferability.",
"location": "Section 4.4",
"category": "coverage",
"focus": "depth"
},
{
"original_text": "The review would be strengthened by including more discussion on how models could be integrated into clinical workflows or real-world settings.",
"improved_version": "Add a section discussing practical strategies for integrating predictive models into clinical workflows, including potential barriers, facilitators, and ethical considerations.",
"explanation": "Bridges research findings with implementation science, increasing translational impact.",
"location": "Section 4.3",
"category": "integration",
"focus": "relevance"
},
{
"original_text": "The review discusses the importance of behavioral features but does not sufficiently explore the potential role of contextual, social, or environmental factors influencing adherence.",
"improved_version": "Incorporate a discussion on how contextual, social, and environmental factors might complement behavioral features in improving prediction accuracy and intervention design.",
"explanation": "Provides a more holistic view of adherence determinants, enriching the analysis.",
"location": "Section 2 & 4.1",
"category": "coverage",
"focus": "breadth"
},
{
"original_text": "The review could include more critical appraisal of the limitations of machine learning approaches, such as overfitting, interpretability, and data bias.",
"improved_version": "Discuss potential ML limitations, including overfitting, interpretability challenges, and biases in training data, and suggest strategies to mitigate these issues.",
"explanation": "Enhances critical evaluation and guides future methodological improvements.",
"location": "Section 4.4",
"category": "analysis",
"focus": "depth"
}
],
"detailed_feedback": {
"coverage_analysis": "The literature review provides a solid overview of quantitative models predicting nonadherence and churn in mHealth interventions, especially emphasizing behavioral engagement data. However, it underrepresents intervention diversity, including different health conditions, populations, and settings, which limits its comprehensiveness. Incorporating studies from varied contexts and qualitative insights would strengthen the breadth and applicability of the review.",
"analysis_quality": "The review demonstrates a good understanding of the technical aspects of ML models and their predictive performance. Nonetheless, it lacks critical appraisal of model limitations, biases, and the real-world challenges of implementation. A more nuanced discussion of these issues would deepen the analytical rigor and provide balanced insights into the translational potential.",
"structure_evaluation": "The manuscript is densely packed with detailed statistical results and technical descriptions, but the organization could be improved with clearer thematic segmentation. Summarizing key findings at the end of sections and using subheadings would enhance readability and guide the reader through complex data, facilitating better synthesis.",
"citation_assessment": "The citations are generally relevant and include recent studies; however, some foundational references are somewhat dated, and a few key recent publications are missing. Updating references and providing critical context for cited works would improve the scholarly rigor and currency of the review.",
"integration_review": "While the review discusses the technical performance of models extensively, it minimally addresses how these models translate into clinical or behavioral outcomes. Strengthening the discussion on practical implementation, ethical considerations, and health impact would improve the connection between research and real-world application."
},
"summary": "Overall, this literature review offers a comprehensive and technically detailed overview of ML-based nonadherence prediction in mHealth interventions. It effectively highlights the predictive capabilities and potential applications but would benefit from deeper critical analysis, broader contextual coverage, clearer organization, and stronger integration with practical health outcomes. These enhancements would elevate its scholarly rigor and translational relevance, making it a more impactful resource for researchers and practitioners."
}

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{
"score": 7,
"critical_remarks": [
{
"category": "design",
"location": "Section 2.2",
"issue": "The study employs a retrospective observational design using historical data, which limits causal inference and may introduce selection bias related to consent and data availability. The rationale for choosing this design over a prospective approach is not explicitly discussed.",
"severity": "high",
"impact": "This limits the ability to establish causality and may affect the generalizability of the findings. It also raises concerns about potential biases in the dataset."
},
{
"category": "methods",
"location": "Section 2.2",
"issue": "The feature selection relies primarily on behavioral engagement metrics, with limited consideration of contextual or sociodemographic factors that could influence adherence, potentially reducing model robustness.",
"severity": "medium",
"impact": "This could lead to omitted variable bias, reducing the predictive accuracy and limiting insights into adherence drivers."
},
{
"category": "analysis",
"location": "Section 2.2 and 3.2",
"issue": "The evaluation metrics focus heavily on AUC, accuracy, and F1-score, but do not sufficiently address calibration or the clinical utility of the models, such as decision thresholds or cost-benefit analyses.",
"severity": "medium",
"impact": "This limits understanding of how models would perform in real-world settings and their practical applicability for intervention."
},
{
"category": "quality",
"location": "Section 4.4",
"issue": "The models are trained and validated on datasets with class imbalance, and while techniques like Tomek Links undersampling are used, there is limited discussion on how this imbalance might bias the results or affect model fairness.",
"severity": "high",
"impact": "Potential bias in model performance, especially in underrepresented groups, which could compromise validity and fairness."
},
{
"category": "ethics",
"location": "Section 2.2 and 4.4",
"issue": "The datasets are anonymized and use consented data, but the manuscript does not detail how participant privacy is protected during data handling, nor does it discuss potential biases introduced by excluding non-consenting users.",
"severity": "medium",
"impact": "This could impact ethical transparency and the reproducibility of the study, as well as the representativeness of the sample."
},
{
"category": "limitations handling",
"location": "Section 4.4",
"issue": "The discussion acknowledges limitations related to data scope and generalizability but lacks specific strategies for addressing these in future research, such as prospective validation or inclusion of diverse populations.",
"severity": "medium",
"impact": "This reduces the clarity on how future work could mitigate current limitations, affecting the study's translational potential."
}
],
"improvement_suggestions": [
{
"original_text": "The study employs a retrospective observational design using historical data, which limits causal inference and may introduce selection bias related to consent and data availability.",
"improved_version": "Consider designing a prospective study to validate the predictive models in real-time settings, which would enhance causal inference and reduce selection bias related to data consent and availability.",
"explanation": "A prospective approach allows for real-time validation and reduces biases inherent in retrospective data, strengthening causal claims and practical relevance.",
"location": "Section 2.1 and 4.4",
"category": "design",
"focus": "approach"
},
{
"original_text": "The feature selection relies primarily on behavioral engagement metrics, with limited consideration of contextual or sociodemographic factors.",
"improved_version": "Incorporate additional features such as sociodemographic variables, health literacy, or baseline health status to enrich the models and improve robustness.",
"explanation": "Adding contextual variables can capture external influences on adherence, potentially increasing model accuracy and interpretability.",
"location": "Section 2.2",
"category": "methods",
"focus": "techniques"
},
{
"original_text": "The evaluation metrics focus heavily on AUC, accuracy, and F1-score, but do not sufficiently address calibration or decision thresholds.",
"improved_version": "Include calibration plots and decision-analytic metrics such as net benefit or cost-effectiveness analyses to assess clinical utility.",
"explanation": "These additions help determine how well the predicted probabilities translate into actionable decisions, improving real-world applicability.",
"location": "Section 2.2 and 3.2",
"category": "analysis",
"focus": "validity"
},
{
"original_text": "Models are trained on imbalanced datasets with techniques like undersampling, but there is limited discussion on bias or fairness across subgroups.",
"improved_version": "Implement fairness-aware modeling approaches and report subgroup performance metrics to assess bias and ensure equitable predictions.",
"explanation": "Addressing potential biases improves model fairness and validity, especially for vulnerable populations.",
"location": "Section 2.2 and 4.4",
"category": "quality",
"focus": "validity"
},
{
"original_text": "The datasets are anonymized and use consented data, but the manuscript does not detail how participant privacy is protected during data handling.",
"improved_version": "Explicitly describe data security measures, anonymization procedures, and compliance with data protection regulations to enhance transparency.",
"explanation": "Clear ethical protocols reinforce trustworthiness and reproducibility of the research.",
"location": "Section 2.2 and 4.4",
"category": "ethics",
"focus": "procedures"
},
{
"original_text": "The models are trained and validated on datasets with class imbalance, and while techniques like undersampling are used, there is limited discussion on how this might bias results.",
"improved_version": "Perform sensitivity analyses using alternative imbalance handling methods (e.g., SMOTE, cost-sensitive learning) and report subgroup performance to evaluate bias.",
"explanation": "This ensures the models are robust and fair across different user groups, improving validity and fairness.",
"location": "Section 2.2 and 4.4",
"category": "quality",
"focus": "validity"
},
{
"original_text": "The study employs a retrospective observational design using historical data, which limits causal inference.",
"improved_version": "Plan future randomized controlled trials or intervention studies to test whether predictive models can effectively inform adherence-promoting strategies.",
"explanation": "Experimental validation would establish causality and practical effectiveness of the models in real-world interventions.",
"location": "Section 4.4",
"category": "design",
"focus": "approach"
},
{
"original_text": "The study does not discuss how user privacy is protected during data collection and analysis.",
"improved_version": "Include detailed descriptions of data anonymization, encryption, and compliance with GDPR or relevant data protection standards to strengthen ethical rigor.",
"explanation": "Transparency about privacy safeguards enhances ethical credibility and reproducibility.",
"location": "Section 2.2 and 4.4",
"category": "ethics",
"focus": "procedures"
},
{
"original_text": "The models are primarily based on behavioral app engagement features, with limited exploration of intervention-specific or social features.",
"improved_version": "Explore incorporating intervention-specific features such as social interactions, messaging frequency, or content engagement to potentially improve predictive performance.",
"explanation": "Adding diverse features may capture additional variance in adherence behavior, enhancing model robustness and utility.",
"location": "Section 2.2",
"category": "methods",
"focus": "techniques"
},
{
"original_text": "The evaluation does not include calibration metrics or decision-analytic assessments.",
"improved_version": "Assess calibration using calibration plots and consider decision curve analysis to evaluate the clinical usefulness of the models.",
"explanation": "These metrics help determine how well predicted probabilities align with actual outcomes and their practical decision-making value.",
"location": "Section 2.2 and 3.2",
"category": "analysis",
"focus": "validity"
},
{
"original_text": "The discussion of limitations does not specify strategies for prospective validation or addressing generalizability concerns.",
"improved_version": "Outline specific plans for prospective validation studies across diverse populations and settings to enhance generalizability and clinical translation.",
"explanation": "Proactive strategies for validation strengthen the evidence base and facilitate real-world implementation.",
"location": "Section 4.4",
"category": "limitations handling",
"focus": "future work"
}
],
"detailed_feedback": {
"design_analysis": "The study adopts a retrospective observational design leveraging existing app engagement data to predict nonadherence and churn. While this approach allows for large-scale analysis and model development, it inherently limits causal inference and may introduce selection bias, especially given the consent-based data collection. The rationale for choosing this design over a prospective or experimental approach is not explicitly discussed, which could impact the interpretability and applicability of the findings.",
"methods_assessment": "The methodology employs machine learning models, primarily random forests, trained on behavioral engagement features such as daily app activity, session counts, and completed exercises. The feature selection is grounded in prior literature emphasizing app activity as a key predictor. Data preprocessing includes normalization and undersampling to address class imbalance. However, the approach omits potentially relevant contextual factors like sociodemographics or health status, which could enhance model robustness. The validation strategy uses stratified 10-fold cross-validation and a train-test split, but the handling of class imbalance, while addressed via undersampling, could benefit from additional techniques like SMOTE or cost-sensitive learning.",
"analysis_evaluation": "Model performance is evaluated using metrics such as AUC, accuracy, F1-score, precision, and recall, which are appropriate for classification tasks. The models demonstrate high predictive accuracy over extended periods, with performance improving as more data accrue. Nonetheless, the analysis lacks calibration assessments and decision-analytic evaluations, which are crucial for translating model outputs into clinical or intervention decisions. The focus on aggregate metrics may obscure subgroup disparities or potential biases in predictions across different user demographics.",
"quality_review": "The study reports on model validity through multiple performance metrics and addresses class imbalance with undersampling. However, there is limited discussion on potential biases introduced by data exclusion (non-consenting users) or imbalance in demographic groups. The models' fairness and generalizability could be compromised if these biases are not thoroughly examined. The reliance on behavioral app data is a strength, but the absence of external validation or prospective testing limits the robustness of the conclusions.",
"ethics_compliance": "The datasets used are anonymized and collected with participant consent, with ethical approval obtained from relevant bodies. The manuscript briefly mentions data privacy measures but does not detail specific procedures such as encryption, anonymization protocols, or compliance with data protection standards like GDPR. Transparency in these areas is essential for ethical rigor and reproducibility, especially given the sensitive nature of health data."
},
"summary": "Overall, this manuscript presents a solid and methodologically sound approach to predicting nonadherence in mHealth interventions using behavioral engagement data and machine learning. The extensive analysis over long durations and across two distinct interventions demonstrates the potential for generalizable predictive models. However, the study's retrospective design, limited inclusion of contextual factors, and lack of calibration and fairness assessments temper the strength of its conclusions. Addressing these issues through prospective validation, broader feature inclusion, and detailed ethical protocols would significantly enhance the rigor, applicability, and impact of this research. Consequently, the manuscript merits a score of 7, reflecting its valuable contributions while acknowledging areas for methodological and ethical refinement."
}

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{
"score": 7,
"critical_remarks": [
{
"category": "presentation",
"location": "Results section, Figures 6.5 and 6.6",
"issue": "Figures are described but not included or clearly labeled with axes, units, or legends, making interpretation difficult.",
"severity": "high",
"impact": "Reduces clarity and hampers the reader's ability to understand model performance trends visually."
},
{
"category": "analysis",
"location": "Model performance metrics, Tables 6.1-6.4",
"issue": "While multiple metrics are reported, there is limited discussion on how these metrics interrelate or their practical significance, especially in the context of clinical or real-world utility.",
"severity": "medium",
"impact": "Weakens the interpretability of the statistical results and their implications for practice."
},
{
"category": "interpretation",
"location": "Discussion, paragraph 4.1",
"issue": "The interpretation of model performance differences (e.g., between nonadherence and churn models) lacks depth regarding potential causes or implications for intervention strategies.",
"severity": "medium",
"impact": "Limits the reader\u2019s understanding of how these findings translate into actionable insights."
},
{
"category": "quality",
"location": "Methods, hyperparameter tuning description",
"issue": "Details on hyperparameter tuning are referenced as in Appendix 6.1 but are not summarized or discussed in the main text, reducing transparency.",
"severity": "low",
"impact": "Slightly diminishes reproducibility and understanding of model optimization procedures."
},
{
"category": "impact",
"location": "Results, predictive performance discussion",
"issue": "Although high performance metrics are reported, the potential for overfitting or model generalizability across different populations is not addressed.",
"severity": "high",
"impact": "Raises concerns about the practical applicability and robustness of the models in diverse real-world settings."
}
],
"improvement_suggestions": [
{
"original_text": "Figures are described but not included or clearly labeled with axes, units, or legends, making interpretation difficult.",
"improved_version": "Include the actual figures with clear labels, axes titles, units, and comprehensive legends. For example, in Figure 6.5, specify 'Model AUC over Weeks 2-13' with axes labeled as 'Week' and 'AUC'.",
"explanation": "Properly labeled and visible figures enhance visual comprehension and allow readers to interpret performance trends directly.",
"location": "Results section, Figures 6.5 and 6.6",
"category": "visualization",
"focus": "clarity"
},
{
"original_text": "While multiple metrics are reported, there is limited discussion on how these metrics interrelate or their practical significance.",
"improved_version": "Add a brief interpretive paragraph explaining how metrics like AUC, F1, precision, and recall collectively inform the model's utility, especially emphasizing their relevance for clinical decision-making or intervention planning.",
"explanation": "Contextualizing statistical metrics helps readers understand their practical implications, making the results more meaningful.",
"location": "Results section, analysis discussion",
"category": "interpretation",
"focus": "clarity"
},
{
"original_text": "The interpretation of performance differences between nonadherence and churn models lacks depth.",
"improved_version": "Expand the discussion to explore potential reasons for performance variations, such as differences in data quality, feature relevance, or inherent behavioral patterns, and discuss how these influence intervention strategies.",
"explanation": "Deeper analysis provides insights into model limitations and guides future improvements or application contexts.",
"location": "Discussion, paragraph 4.1",
"category": "interpretation",
"focus": "interpretation"
},
{
"original_text": "Details on hyperparameter tuning are referenced as in Appendix 6.1 but are not summarized in the main text.",
"improved_version": "Provide a concise summary of hyperparameter tuning strategies in the main text, such as the range of parameters tested and the selection criteria, to improve transparency.",
"explanation": "Summarizing key methodological steps enhances reproducibility and reader understanding of model development.",
"location": "Methods section",
"category": "quality",
"focus": "clarity"
},
{
"original_text": "High performance metrics are reported without addressing overfitting or generalizability.",
"improved_version": "Include a discussion on potential overfitting, such as validation strategies used, and comment on the models' expected performance in external or real-world datasets.",
"explanation": "Addressing model robustness increases confidence in the applicability of the findings and guides future validation efforts.",
"location": "Discussion, model robustness",
"category": "impact",
"focus": "significance"
},
{
"original_text": "Results on model performance are extensive but lack a summary table or visual summary for quick reference.",
"improved_version": "Add a summary table or dashboard figure that consolidates key performance metrics across all prediction windows for easy comparison.",
"explanation": "Visual summaries facilitate quick understanding of overall model performance and trends.",
"location": "Results section, summary visualization",
"category": "visualization",
"focus": "clarity"
},
{
"original_text": "The discussion of feature importance is limited to Appendix 6.2 without highlighting key features in the main text.",
"improved_version": "Summarize the most important features influencing predictions directly in the main text, such as 'daily exercise completion' and 'last login activity,' and discuss their practical relevance.",
"explanation": "Highlighting key features emphasizes their role and supports understanding of behavioral drivers of nonadherence.",
"location": "Results/discussion, feature importance",
"category": "interpretation",
"focus": "clarity"
},
{
"original_text": "The manuscript does not explicitly address potential biases or limitations related to data collection or model applicability.",
"improved_version": "Add a paragraph discussing limitations such as selection bias (e.g., only consenting users), potential data sparsity in less engaged populations, and the need for external validation.",
"explanation": "Acknowledging limitations provides a balanced view and guides future research directions.",
"location": "Discussion, limitations",
"category": "quality",
"focus": "significance"
},
{
"original_text": "The implications for practice are mentioned but could be expanded with concrete examples of how models could be integrated into intervention workflows.",
"improved_version": "Include specific examples, such as 'integrating real-time predictions into app notifications to prompt users at risk of nonadherence,' to illustrate practical application.",
"explanation": "Concrete examples make the potential impact more tangible and guide implementation efforts.",
"location": "Discussion, implications",
"category": "impact",
"focus": "significance"
},
{
"original_text": "The overall presentation of results is dense, with extensive data tables and narrative, which could overwhelm readers.",
"improved_version": "Streamline the presentation by highlighting key findings in summary boxes or infographics and relegating detailed tables to supplementary materials, with clear cross-references.",
"explanation": "Simplified presentation improves readability and emphasizes main messages without sacrificing detail.",
"location": "Results section",
"category": "presentation",
"focus": "clarity"
}
],
"detailed_feedback": {
"presentation_analysis": "The results are comprehensive, including multiple performance metrics, descriptive statistics, and detailed tables. However, the figures referenced are not included or well-labeled in the main text, which hampers visual interpretation. The dense data presentation could benefit from more visual summaries or simplified tables to enhance clarity and reader engagement.",
"analysis_quality": "The statistical analysis employs appropriate machine learning algorithms, cross-validation, and hyperparameter tuning. Performance metrics like AUC, accuracy, F1, precision, and recall are thoroughly reported across multiple prediction windows. Nonetheless, there is limited discussion on potential overfitting, model robustness, or external validation, which are critical for assessing real-world applicability.",
"interpretation_review": "The discussion provides a solid overview of model performance and potential applications. However, it lacks depth in interpreting differences between models, such as why nonadherence models outperform churn models in certain contexts, and how these insights translate into intervention strategies. Expanding on the behavioral significance of key features would strengthen the interpretive value.",
"visualization_assessment": "Figures are described but not included, and the tables are extensive but dense, making it difficult for readers to quickly grasp key trends. Incorporating visual summaries like performance trend graphs, feature importance charts, or consolidated performance dashboards would significantly improve comprehension.",
"significance_evaluation": "The high performance metrics suggest promising predictive capabilities, but the manuscript does not sufficiently address issues of overfitting, model generalizability, or applicability across diverse populations. Explicit discussion of these aspects would enhance the perceived significance and practical relevance of the findings."
},
"summary": "Overall, this manuscript presents a valuable and methodologically sound exploration of nonadherence prediction in mHealth interventions. The extensive data and rigorous analysis support the potential utility of behavioral engagement features combined with machine learning. To elevate the manuscript, improvements in data visualization, deeper interpretive discussion, and explicit acknowledgment of limitations and generalizability are recommended. These enhancements would make the findings more accessible, actionable, and robust, aligning with the high standards expected for impactful research in digital health."
}

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{
"score": 7,
"critical_remarks": [
{
"category": "interpretation",
"location": "Section 4.1 - Result interpretation",
"issue": "While the discussion reports high prediction accuracy, it lacks a nuanced analysis of the clinical or practical significance of these metrics, such as how they translate into real-world intervention effectiveness.",
"severity": "medium",
"impact": "This limits the reader's understanding of how the predictive performance impacts actual adherence behavior change or health outcomes."
},
{
"category": "context",
"location": "Section 4.1 - Literature comparison",
"issue": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.",
"severity": "medium",
"impact": "Reduces the depth of contextual understanding and may overstate the novelty or robustness of findings."
},
{
"category": "reflection",
"location": "Section 4.4 - Limitations and future work",
"issue": "The limitations are acknowledged but lack specific strategies for addressing these issues in future research, such as how to handle sparse data or how to validate models prospectively.",
"severity": "high",
"impact": "Weakens the manuscript's guidance for subsequent research and practical implementation."
},
{
"category": "impact",
"location": "Section 4.3 - Practical implications",
"issue": "While potential strategies are discussed, there is limited discussion on the actual feasibility, cost-effectiveness, or ethical considerations of implementing predictive interventions in real-world settings.",
"severity": "medium",
"impact": "Reduces the translational value and real-world relevance of the findings."
},
{
"category": "quality",
"location": "Throughout the discussion",
"issue": "The discussion is dense with technical details and statistical results but often lacks coherence and clear synthesis, making it difficult for readers to grasp the overarching narrative or implications.",
"severity": "high",
"impact": "Impairs clarity and diminishes the overall readability and impact of the manuscript."
}
],
"improvement_suggestions": [
{
"original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).",
"improved_version": "Our results demonstrate that nonadherence, including complete discontinuation (churn), can be predicted with high accuracy over extended periods. This aligns with and extends prior definitions by Sieverink et al. (2017), emphasizing the potential for early intervention strategies.",
"explanation": "Clarifies the significance of the findings and explicitly links them to existing conceptual frameworks, enhancing interpretability.",
"location": "Section 4.1",
"category": "interpretation",
"focus": "significance"
},
{
"original_text": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.",
"improved_version": "While our findings align with previous research on churn prediction, differences in intervention types, populations, and data collection methods should be considered. For example, prior studies often focus on shorter durations or different app domains, which may influence predictive performance.",
"explanation": "Provides a more nuanced and critical comparison, acknowledging contextual differences that affect generalizability.",
"location": "Section 4.1",
"category": "context",
"focus": "comparison"
},
{
"original_text": "A shared characteristic of both analyzed mHealth interventions is that users must provide proof of medical need to access the apps and receive reimbursement. User behavior in these contexts may differ from that observed in mHealth interventions directly available in public app stores, where users can self-enroll.",
"improved_version": "The requirement for proof of medical need and reimbursement access may influence user engagement patterns, potentially leading to higher retention rates compared to freely available apps in public stores. Future studies should examine how onboarding and access mechanisms impact adherence trajectories.",
"explanation": "Highlights a specific contextual factor that could influence generalizability and suggests a direction for future research.",
"location": "Section 4.4",
"category": "reflection",
"focus": "limitations/future work"
},
{
"original_text": "The models rely on rich, continuous behavioral app engagement data, which may not be available in all settings.",
"improved_version": "The predictive models depend on detailed, continuous engagement data, which may limit applicability in interventions with sporadic or sparse data collection. Developing models that incorporate less granular data or alternative indicators could broaden usability.",
"explanation": "Addresses a key limitation and proposes a constructive solution, enhancing the practical relevance of future work.",
"location": "Section 4.4",
"category": "reflection",
"focus": "limitations/future work"
},
{
"original_text": "While the results are promising, prospective trials are necessary to fully establish the applicability of these models.",
"improved_version": "To validate the predictive utility and real-world impact of these models, prospective randomized trials incorporating intervention components based on model predictions are essential. Such studies should assess not only adherence but also health outcomes.",
"explanation": "Provides a clear, actionable future research direction that emphasizes validation and impact assessment.",
"location": "Section 4.4",
"category": "reflection",
"focus": "future work"
},
{
"original_text": "Our discussion reports high prediction accuracy, but it lacks a nuanced analysis of the clinical or practical significance of these metrics.",
"improved_version": "Although the models demonstrate high accuracy, translating these metrics into meaningful clinical or behavioral improvements requires further investigation. For instance, understanding how early predictions influence adherence behaviors and health outcomes will be critical for implementation.",
"explanation": "Bridges statistical performance with practical significance, guiding future translational efforts.",
"location": "Section 4.1",
"category": "interpretation",
"focus": "implications"
},
{
"original_text": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.",
"improved_version": "Future research should systematically compare models across diverse populations and intervention types to identify factors influencing predictive accuracy and generalizability, including demographic, behavioral, and contextual variables.",
"explanation": "Encourages comprehensive comparative analyses, strengthening the contextual understanding.",
"location": "Section 4.1",
"category": "context",
"focus": "comparison"
},
{
"original_text": "The discussion is dense with technical details and statistical results but often lacks coherence and clear synthesis.",
"improved_version": "To enhance clarity, we recommend structuring the discussion around key themes: the predictive performance, practical implications, limitations, and future directions. Summarizing main points at the end of each subsection can improve readability.",
"explanation": "Improves overall coherence and helps readers synthesize complex information.",
"location": "Throughout the discussion",
"category": "quality",
"focus": "coherence"
},
{
"original_text": "The models' performance metrics are high, but the discussion does not sufficiently address the potential for false positives and negatives in clinical decision-making.",
"improved_version": "While the models show high predictive accuracy, the implications of false positives and negatives\u2014such as unnecessary interventions or missed opportunities\u2014should be carefully considered. Future work should evaluate the cost-benefit balance of model deployment in practice.",
"explanation": "Adds critical reflection on the limitations of predictive accuracy and its impact on real-world decisions.",
"location": "Section 4.1",
"category": "impact",
"focus": "implications"
},
{
"original_text": "The discussion could benefit from explicitly linking the findings to health outcomes, which remains an open question.",
"improved_version": "Ultimately, establishing a direct link between early adherence predictions and improved health outcomes remains an important avenue for future research. Integrating predictive models within adaptive intervention frameworks could facilitate this goal.",
"explanation": "Clarifies the ultimate translational goal and guides future research priorities.",
"location": "Section 4.4",
"category": "impact",
"focus": "significance"
}
],
"detailed_feedback": {
"interpretation_analysis": "The discussion effectively highlights the high predictive accuracy of models over extended durations, emphasizing their potential for early intervention. However, it would benefit from a deeper analysis of how these metrics translate into meaningful behavioral or health improvements, including potential thresholds for clinical utility and the impact of false positives/negatives on users and providers.",
"context_review": "The manuscript references prior studies to support its findings but often treats these comparisons superficially. A more critical analysis considering differences in intervention types, populations, and data collection methods would strengthen claims of novelty and generalizability. Explicit discussion of how the current work advances or diverges from existing literature is needed.",
"reflection_assessment": "Limitations such as data dependency, generalizability, and the need for prospective validation are acknowledged but lack specific strategies for addressing them. Future research directions should include plans for validation in real-world settings, handling sparse data, and expanding to diverse populations. The discussion should also consider ethical and practical challenges of implementing predictive models in clinical workflows.",
"impact_evaluation": "The discussion appropriately discusses the potential for targeted strategies but underestimates practical challenges such as cost, user acceptance, and ethical considerations. Explicitly addressing these factors would enhance the translational relevance. Additionally, linking prediction performance to health outcomes would clarify the clinical significance of the models.",
"quality_analysis": "While comprehensive in technical detail, the discussion suffers from a lack of clear structure and synthesis. Organizing content around key themes\u2014performance, implications, limitations, future directions\u2014and summarizing main points would improve readability. Reducing jargon and providing concise takeaways would make the discussion more accessible."
},
"summary": "Overall, the discussion demonstrates solid technical analysis and acknowledges key limitations, but it would benefit from deeper interpretation of results, critical comparison with existing literature, clearer structure, and explicit guidance for future research and practical application. These enhancements would elevate the manuscript's clarity, impact, and translational potential."
}

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{
"score": 7,
"critical_remarks": [
{
"category": "support",
"location": "Section 4.1",
"issue": "While the conclusion states that models can predict nonadherence accurately, it lacks explicit mention of how the results directly support the specific performance metrics reported, such as the high AUC and F1 scores, especially in the context of real-world applicability.",
"severity": "medium",
"impact": "This reduces confidence in the strength of the evidence backing the claims, making the support less transparent."
},
{
"category": "objectives",
"location": "Section 4.1",
"issue": "The conclusion claims the study extends prior research but does not explicitly restate the original research objectives or how they were fulfilled beyond the general statement.",
"severity": "low",
"impact": "This diminishes clarity about the specific aims achieved, affecting the perceived completeness of the conclusion."
},
{
"category": "implications",
"location": "Section 4.2",
"issue": "Implications for practice are discussed, but the potential limitations or challenges of implementing these predictive models in real-world settings are underexplored.",
"severity": "medium",
"impact": "This limits the practical relevance and may overstate the readiness for application."
},
{
"category": "presentation",
"location": "Section 4.4",
"issue": "The conclusion contains dense, lengthy sentences that could be more concise and clearer, reducing overall readability.",
"severity": "low",
"impact": "This affects the clarity and impact of the final message."
}
],
"improvement_suggestions": [
{
"original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).",
"improved_version": "Our results demonstrate that nonadherence, including complete discontinuation (churn), can be reliably predicted over long-term intervention periods, aligning with the definitions by Sieverink et al. (2017).",
"explanation": "This clarification emphasizes the robustness of the findings and explicitly links the results to the operational definitions used, strengthening support clarity.",
"location": "Section 4.1",
"category": "support",
"focus": "clarity"
},
{
"original_text": "Given the conceptual link between churn and nonadherence \u2013 where fully disengaged users are inherently nonadherent \u2013 these results are intuitive.",
"improved_version": "Since churn represents a complete disengagement, it logically aligns with nonadherence, making the high prediction accuracy expected and reinforcing the validity of our approach.",
"explanation": "This rephrasing explicitly states the conceptual link, enhancing the logical support for the results and their interpretation.",
"location": "Section 4.1",
"category": "support",
"focus": "support"
},
{
"original_text": "Our descriptive analysis further emphasizes this relationship, showing that the decline in adherence over time in Vivira and Manoa is largely driven by churn (i.e., users discontinuing entire use).",
"improved_version": "Our descriptive data confirms that the decline in adherence over time is primarily due to user churn, supporting the predictive models' focus on identifying full discontinuation.",
"explanation": "This explicitly ties the descriptive findings to the predictive modeling, reinforcing the support from empirical data.",
"location": "Section 4.1",
"category": "support",
"focus": "support"
},
{
"original_text": "In contrast, the performance of nonadherence prediction models in Manoa was comparatively lower, correctly identifying an average of 86% (SD = 7.6%, mean AUC = 0.82) of nonadherent users between Months 2 and 6 at a relatively higher false positive rate of 49.5% (SD = 12.9%).",
"improved_version": "In Manoa, nonadherence prediction accuracy was somewhat lower, with models correctly identifying 86% of nonadherent users (AUC = 0.82), though with a higher false positive rate of 49.5%, indicating room for improvement.",
"explanation": "This clarifies the performance limitations and contextualizes the results, providing a balanced view that supports a nuanced interpretation.",
"location": "Section 4.1",
"category": "support",
"focus": "clarity"
},
{
"original_text": "Feature importance analyses across all prediction models showed that behavioral app engagement data collected closer to the prediction event had a stronger impact on model performance.",
"improved_version": "Analysis of feature importance revealed that behavioral engagement data obtained nearer to the prediction point significantly enhanced model performance.",
"explanation": "This concise rephrasing improves clarity and emphasizes the temporal relevance of data, supporting the methodological insights.",
"location": "Section 4.1",
"category": "support",
"focus": "clarity"
},
{
"original_text": "Our findings further show that the predictive performance of daily churn prediction models improves over time as more behavioral app engagement data becomes available.",
"improved_version": "Our results indicate that daily churn prediction models become more accurate over time, as additional behavioral engagement data accumulates, supporting their robustness.",
"explanation": "This enhances clarity and explicitly states the causal relationship, strengthening support from the results.",
"location": "Section 4.1",
"category": "support",
"focus": "support"
},
{
"original_text": "While nonadherence prediction models were more adept at identifying nonadherent users who had already churned, they also correctly identified a substantial proportion of nonadherent users before they recorded their last log in.",
"improved_version": "Although models predicted users who had already churned with higher accuracy, they also successfully identified many users at risk of nonadherence before complete disengagement.",
"explanation": "This clarifies the predictive capacity at different stages, emphasizing the practical utility for early intervention.",
"location": "Section 4.2",
"category": "support",
"focus": "clarity"
},
{
"original_text": "In summary, churn prediction models demonstrated strong and improving performance throughout the observation period.",
"improved_version": "In conclusion, the churn prediction models showed strong, progressively improving performance over the entire observation window.",
"explanation": "This concise summary reinforces the key finding with emphasis on the temporal improvement, enhancing clarity and impact.",
"location": "Section 4.1",
"category": "presentation",
"focus": "strength"
},
{
"original_text": "The results of our study demonstrate that ML algorithms, when paired with behavioral app engagement data, can generate actionable insights that may support targeted strategies for preventing nonadherence.",
"improved_version": "Our study confirms that machine learning algorithms utilizing behavioral engagement data can produce actionable insights to inform targeted adherence-promoting strategies.",
"explanation": "This improves clarity and emphasizes the practical relevance of the findings.",
"location": "Section 4.3",
"category": "implications",
"focus": "clarity"
},
{
"original_text": "Previous research has consistently ranked features related to user app activity (e.g., daily logins) and app progress (e.g., completion of app-logged activities) as the most important for churn prediction in mHealth interventions [5, 21] and other app domains [7, 27, 29, 41, 56].",
"improved_version": "Consistent with prior studies [5, 21, 7, 27, 29, 41, 56], our findings highlight that user activity and progress features are the most predictive for churn in mHealth and other app domains.",
"explanation": "This consolidates evidence, making the statement more concise and emphasizing the generalizability of the feature importance.",
"location": "Section 4.3",
"category": "support",
"focus": "support"
},
{
"original_text": "Future research should aim to replicate nonadherence prediction models in diverse mHealth contexts and evaluate their integration with targeted preventive strategies in prospective trials to assess the impact of these combined approaches on app usage, adherence, and health outcomes.",
"improved_version": "Future studies should validate these nonadherence prediction models across varied mHealth settings and test their integration with targeted interventions in prospective trials to evaluate effects on engagement and health outcomes.",
"explanation": "This streamlines the statement, making it more actionable and clearer about the next steps for research.",
"location": "Section 4.4",
"category": "future_directions",
"focus": "clarity"
}
],
"detailed_feedback": {
"support_analysis": "The conclusion effectively summarizes the high predictive accuracy of the models, supported by reported metrics such as AUCs above 0.8 and F1 scores above 0.8. It also references the consistency of these results across two different interventions, reinforcing the robustness of the support. However, explicit links between specific results and their implications for real-world application could be strengthened to enhance transparency.",
"objective_fulfillment": "The conclusion addresses the primary objectives of demonstrating the predictive capacity of behavioral app engagement data for nonadherence and extending prior research to longer durations. It clearly states that the study fulfills these aims, although explicitly restating the original research questions would improve clarity on objective fulfillment.",
"implications_analysis": "The discussion of implications highlights the potential for targeted strategies and the generalizability of models. Nonetheless, it underplays the practical challenges, such as implementation barriers, user privacy concerns, and variability in real-world settings, which are critical for translating findings into practice.",
"presentation_analysis": "While comprehensive, the conclusion is somewhat verbose, with complex sentences that could be simplified for better readability. The overall structure could benefit from more concise summaries and clearer topic transitions to improve flow and impact.",
"contribution_analysis": "The manuscript clearly emphasizes that this is the first study to evaluate nonadherence prediction aligned with Sieverink et al.'s definition over extended periods, contributing novel insights. However, explicitly stating how this advances the field beyond prior models would further clarify its unique contribution."
},
"summary": "Overall, the conclusion effectively summarizes key findings, supports claims with robust data, and discusses relevant implications. However, it would benefit from clearer linkage between results and claims, more balanced discussion of practical limitations, and improved conciseness. These enhancements would elevate the manuscript's clarity, support strength, and contribution clarity, making it more impactful and comprehensive."
}

14
Backup/V5_multi_agent2/results/S9_results.json
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@@ -1,14 +0,0 @@
{
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"accuracy_analysis": "",
"completeness_analysis": "",
"format_analysis": "",
"quality_analysis": "",
"organization_analysis": ""
},
"summary": "Error in analysis: Error analyzing references: Unterminated string starting at: line 13 column 19 (char 600)",
"error": true
}

162
Backup/V5_multi_agent2/results/W1_results.json
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@@ -1,162 +0,0 @@
{
"language_style_score": 7,
"critical_remarks": [
{
"category": "grammar",
"location": "Abstract, paragraph 4",
"issue": "Inconsistent use of hyphenation in 'noncommunicable' and 'nonadherence'.",
"severity": "low",
"impact": "Minor inconsistency; standardization improves professionalism."
},
{
"category": "spelling",
"location": "Introduction, paragraph 2",
"issue": "The term 'hyperglycemia' is spelled correctly, but in some instances, similar terms like 'hyperglycemia' are misspelled as 'hyperglycemia' (e.g., in references).",
"severity": "low",
"impact": "Ensures consistency and accuracy in terminology, maintaining credibility."
},
{
"category": "punctuation",
"location": "Results, section 3.2.1",
"issue": "Numerous instances of missing commas after introductory phrases, e.g., 'Across the prediction windows from Week 2 to Week 13, the models achieved...'",
"severity": "medium",
"impact": "Reduces clarity and can cause reader confusion."
},
{
"category": "sentence_structure",
"location": "Discussion, paragraph 4.1",
"issue": "Long, complex sentences that could be split for clarity, e.g., the sentence starting with 'Given the conceptual link...'",
"severity": "medium",
"impact": "Improves readability and comprehension."
},
{
"category": "verb_tense",
"location": "Methodology, section 2.2",
"issue": "Inconsistent use of past and present tense, e.g., 'We selected features' (past) and 'we predict nonadherence' (present).",
"severity": "low",
"impact": "Consistency enhances professional tone and clarity."
},
{
"category": "subject-verb",
"location": "Results, section 3.2.2",
"issue": "In several instances, plural subjects are paired with singular verbs, e.g., 'the models achieved a mean AUC...' (correct), but in some cases, agreement is inconsistent.",
"severity": "low",
"impact": "Maintains grammatical correctness and professionalism."
},
{
"category": "articles",
"location": "Introduction, paragraph 2",
"issue": "Incorrect or missing articles, e.g., 'a systematic review of effectivity' should be 'a systematic review of effectiveness'.",
"severity": "low",
"impact": "Improves grammatical accuracy and clarity."
},
{
"category": "prepositions",
"location": "Discussion, paragraph 4.2",
"issue": "Incorrect preposition usage, e.g., 'detect users in the early stages of disengagement' could be 'detect users at the early stages of disengagement'.",
"severity": "low",
"impact": "Enhances precision and correctness of expression."
},
{
"category": "conjunctions",
"location": "Discussion, paragraph 4.4",
"issue": "Overuse of 'however' at the beginning of sentences, which can be stylistically repetitive.",
"severity": "low",
"impact": "Varying conjunctions improves flow and style."
},
{
"category": "academic_conventions",
"location": "Throughout the document",
"issue": "Inconsistent formatting of references and in-text citations, e.g., some citations are bracketed, others are not.",
"severity": "medium",
"impact": "Reduces professionalism and adherence to academic standards."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "The rich data collected by mHealth interventions raise the question of whether\u2014and to what extent\u2014nonadherence can be predicted using these data.",
"explanation": "Replacing hyphens with em dashes and removing spaces improves punctuation consistency and formal style.",
"location": "Abstract, paragraph 1",
"category": "punctuation",
"focus": "punctuation"
},
{
"original_text": "In the German SHI system [46].",
"improved_version": "In the German SHI system [46].",
"explanation": "Remove the period after the bracketed citation to adhere to standard referencing style.",
"location": "Methodology, section 2.1.1",
"category": "academic_conventions",
"focus": "citation format"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.",
"improved_version": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), where nonadherence was defined as completing fewer than eight therapeutic exercises per week.",
"explanation": "Clarifies the definition of nonadherence and improves sentence flow by restructuring.",
"location": "Results, section 3.2.1",
"category": "sentence_structure",
"focus": "clarity"
},
{
"original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.",
"improved_version": "The use of DiGA data is strictly limited; therefore, only users who provided consent under Article 4, Section 2, and 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.",
"explanation": "Using a semicolon improves sentence connection; adding 'and' clarifies the list structure.",
"location": "Methodology, section 2.1.1",
"category": "punctuation",
"focus": "punctuation"
},
{
"original_text": "In Vivira, we predicted nonadherence weekly from Weeks 2 to 13 based on users\u2019 daily app activity variables (active or inactive) and the daily number of completed exercises variables (continuous) of the preceding weeks.",
"improved_version": "In Vivira, we predicted nonadherence weekly from Weeks 2 to 13, based on users\u2019 daily app activity variables (active or inactive) and the daily number of completed exercises (continuous) from the preceding weeks.",
"explanation": "Adding a comma improves readability; removing 'variables' after 'exercises' avoids redundancy.",
"location": "Methodology, section 2.2",
"category": "sentence_structure",
"focus": "clarity"
},
{
"original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).",
"improved_version": "Our findings demonstrate that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence\u2014relative to the intended use as defined by Sieverink et al. (2017)\u2014and in its most severe form: churn (i.e., complete discontinuation of use).",
"explanation": "Using em dashes and colons clarifies the structure and emphasizes key definitions, enhancing readability.",
"location": "Discussion, paragraph 4.1",
"category": "punctuation",
"focus": "punctuation"
},
{
"original_text": "The article 'predicts nonadherence' (present tense) but elsewhere, the methodology is described in past tense.",
"improved_version": "Ensure consistent tense usage throughout; for example, use past tense ('predicted') when describing the methodology and results, and present tense ('predicts') only when discussing general truths or ongoing implications.",
"explanation": "Consistency in verb tense aligns with academic standards and clarifies temporal context.",
"location": "General, throughout the document",
"category": "verb_tense",
"focus": "tense consistency"
},
{
"original_text": "The models achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of 0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and a mean recall of 0.86 (SD = 0.14) across all prediction points at the of Day 1 until 178, indicating a robust ability to discriminate between churned and non-churned users.",
"improved_version": "The models achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of 0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and a mean recall of 0.86 (SD = 0.14) across all prediction points from Day 1 until Day 178, indicating a robust ability to discriminate between churned and non-churned users.",
"explanation": "Corrects grammatical error ('at the of Day 1' to 'from Day 1') and improves clarity of time frame.",
"location": "Results, section 3.2.2",
"category": "grammar",
"focus": "grammar"
},
{
"original_text": "The overall assessment paragraph.",
"improved_version": "Overall, the analysis indicates that the manuscript demonstrates solid methodological rigor, clear structure, and comprehensive coverage of the topic, with room for minor stylistic and grammatical improvements to enhance clarity and professionalism.",
"explanation": "Provides a concise, balanced summary of the overall quality and areas for improvement.",
"location": "Summary",
"category": "general",
"focus": "overall assessment"
}
],
"detailed_feedback": {
"grammar_correctness": "The manuscript generally maintains grammatical correctness, but some complex sentences could be simplified for clarity. Minor issues include inconsistent tense usage and occasional awkward phrasing that can be smoothed for better flow.",
"spelling_accuracy": "Spelling is accurate throughout; however, consistency in hyphenation (e.g., 'noncommunicable') and terminology (e.g., 'hyperglycemia') should be maintained. Also, ensure all references to technical terms are spelled uniformly.",
"punctuation_usage": "Punctuation is mostly correct but can be improved by standardizing the use of em dashes, commas, and semicolons, especially in complex sentences. Proper punctuation enhances readability and formal tone.",
"sentence_structure": "Many sentences are lengthy and contain multiple ideas, which can be split into shorter, clearer sentences. This improves readability and reduces cognitive load for the reader.",
"verb_tense_consistency": "The manuscript shifts between past and present tense, particularly when describing methods and results. Maintaining consistent tense\u2014preferably past tense for completed actions\u2014would improve clarity and professionalism.",
"subject-verb_agreement": "Subject-verb agreement is generally correct; however, careful review is recommended in complex sentences, especially where collective nouns or multiple subjects are involved.",
"article_usage": "Articles are mostly used correctly, but some instances lack definite or indefinite articles where needed, affecting grammatical correctness and clarity.",
"preposition_usage": "Prepositions are generally appropriate, but some phrases could be more precise, e.g., 'predict users in the early stages' should be 'predict users at the early stages'.",
"conjunction_usage": "Conjunctions are used appropriately, but overuse of 'however' at sentence starts can be stylistically monotonous. Varying conjunctions can improve flow.",
"academic_conventions": "Citation formatting is inconsistent; some references lack proper punctuation or formatting. Standardizing reference style and citation placement will enhance professionalism."
},
"summary": "The manuscript demonstrates strong academic writing with clear structure and comprehensive content. Minor language and stylistic improvements\u2014such as standardizing punctuation, tense, and reference formatting\u2014would elevate the clarity, professionalism, and readability of the work. Addressing these issues will ensure the manuscript aligns with high academic standards and enhances its impact."
}

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Backup/V5_multi_agent2/results/W2_results.json
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@@ -1,202 +0,0 @@
{
"narrative_structure_score": 6,
"critical_remarks": [
{
"category": "narrative_coherence",
"location": "Abstract and Introduction",
"issue": "The abstract provides a comprehensive overview but lacks explicit linkage to the specific research questions or hypotheses, making the narrative feel somewhat disconnected from the detailed objectives outlined later.",
"severity": "medium",
"impact": "This weakens the coherence between the abstract and the main research aims, potentially confusing readers about the core focus."
},
{
"category": "logical_progression",
"location": "Results and Discussion",
"issue": "Results are detailed extensively but are not always clearly connected back to the hypotheses or research questions, leading to a fragmented understanding of how findings support or challenge initial assumptions.",
"severity": "high",
"impact": "This hampers the logical flow from data presentation to interpretation, reducing clarity of the overall narrative."
},
{
"category": "transitions",
"location": "Between sections (e.g., Methods to Results, Results to Discussion)",
"issue": "Transitions are abrupt; for example, the shift from detailed prediction results to their implications in the discussion lacks smooth linking sentences.",
"severity": "medium",
"impact": "This disrupts the reader\u2019s flow, making the narrative feel disjointed and harder to follow."
},
{
"category": "paragraph_organization",
"location": "Methods and Results sections",
"issue": "Some paragraphs, especially in the Methods, are overly dense with technical details, which could be better organized into subsections for clarity.",
"severity": "medium",
"impact": "This reduces readability and hampers quick comprehension of key methodological steps."
},
{
"category": "topic_sentences",
"location": "Multiple sections",
"issue": "Many paragraphs lack clear topic sentences that outline their main point, especially in the Results and Discussion sections.",
"severity": "high",
"impact": "This diminishes the guiding thread for the reader, making it difficult to grasp the purpose of each paragraph."
},
{
"category": "evidence_integration",
"location": "Results and Discussion",
"issue": "While extensive data are presented, there is limited integration of evidence with interpretative commentary, especially in linking statistical results to practical implications.",
"severity": "high",
"impact": "This weakens the persuasive power of the narrative and obscures the significance of findings."
},
{
"category": "conclusion_alignment",
"location": "Conclusion",
"issue": "The conclusion summarizes findings but does not explicitly revisit the initial hypotheses or research questions, leading to a slight misalignment with the introduction.",
"severity": "medium",
"impact": "This reduces the narrative closure and clarity of how the study addresses its initial aims."
},
{
"category": "hypothesis_tracking",
"location": "Throughout the paper",
"issue": "Explicit tracking of hypotheses or research questions is inconsistent; some sections discuss findings without clearly referencing the original aims.",
"severity": "high",
"impact": "This hampers the reader\u2019s ability to follow how evidence supports or refutes specific hypotheses."
},
{
"category": "visual_integration",
"location": "Figures and Tables",
"issue": "Figures and tables are numerous and detailed but lack direct references or explanations within the text that clarify their relevance to the narrative.",
"severity": "medium",
"impact": "This diminishes their effectiveness as visual aids and can cause confusion about their purpose."
},
{
"category": "reader_engagement",
"location": "Entire document",
"issue": "The dense technical language and extensive data presentation may overwhelm readers, reducing engagement.",
"severity": "medium",
"impact": "This could lead to decreased comprehension and interest, especially for non-specialist audiences."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "Building on the rich data collected by mHealth interventions, this study investigates whether and to what extent nonadherence can be accurately predicted, directly addressing this key question.",
"explanation": "Clarifies the purpose of the sentence, linking it explicitly to the research aim and enhancing narrative clarity.",
"location": "Abstract",
"category": "abstract",
"focus": "narrative_coherence"
},
{
"original_text": "In the Introduction, the flow from the general context of NCDs to the specific focus on mHealth adherence is somewhat abrupt.",
"improved_version": "The rising prevalence and economic burden of noncommunicable diseases (NCDs) underscore the urgent need for scalable interventions. Among these, mobile health (mHealth) solutions have gained prominence due to their accessibility and potential to support disease management, yet adherence remains a critical challenge that this study aims to address.",
"explanation": "Creates a smoother logical progression from broad context to specific research focus, improving coherence.",
"location": "Introduction",
"category": "introduction",
"focus": "logical_progression"
},
{
"original_text": "Results are detailed extensively but are not always clearly connected back to the hypotheses or research questions.",
"improved_version": "The results demonstrate that the machine learning models achieved high accuracy in predicting nonadherence, supporting the hypothesis that behavioral engagement data can serve as reliable predictors of user adherence patterns.",
"explanation": "Explicitly links findings to research hypotheses, strengthening the logical flow.",
"location": "Results",
"category": "results",
"focus": "hypothesis_tracking"
},
{
"original_text": "The transition between the Results and Discussion sections is abrupt.",
"improved_version": "These predictive performance metrics highlight the models' robustness over extended periods. In the following discussion, we interpret these findings in the context of their practical implications for intervention strategies and future research directions.",
"explanation": "Provides a linking sentence that guides the reader smoothly from data to interpretation.",
"location": "End of Results / Beginning of Discussion",
"category": "transitions",
"focus": "transitions"
},
{
"original_text": "Many paragraphs in Methods are overly dense with technical details.",
"improved_version": "The Methods section is organized into clear subsections: Data Collection, Feature Selection, Model Training, and Evaluation. Each subsection provides concise descriptions of the procedures, enhancing readability.",
"explanation": "Improves paragraph organization and readability by structuring technical content into subsections.",
"location": "Methods",
"category": "methodology",
"focus": "paragraph_organization"
},
{
"original_text": "Many paragraphs lack clear topic sentences.",
"improved_version": "This paragraph introduces the primary features used for prediction, emphasizing their relevance to the intervention's objectives.",
"explanation": "Adding explicit topic sentences guides the reader through complex sections, improving clarity.",
"location": "Multiple sections",
"category": "topic_sentences",
"focus": "topic_sentence_effectiveness"
},
{
"original_text": "Figures and tables are numerous but often lack references within the text.",
"improved_version": "Each figure and table is explicitly referenced in the text, e.g., 'As shown in Figure 6.5, the model performance improves steadily over weeks,' ensuring the visual data supports the narrative.",
"explanation": "Explicit references improve visual integration and help readers connect data with narrative points.",
"location": "Figures/Tables",
"category": "visual_integration",
"focus": "visual_element_integration"
},
{
"original_text": "The discussion section summarizes findings but does not revisit initial hypotheses.",
"improved_version": "The discussion revisits the initial hypotheses, confirming that behavioral engagement data can reliably predict nonadherence and emphasizing the implications for targeted intervention strategies.",
"explanation": "Aligns conclusions with initial aims, reinforcing narrative closure.",
"location": "Discussion",
"category": "conclusion_alignment",
"focus": "conclusion_alignment"
},
{
"original_text": "The narrative does not explicitly track the hypotheses throughout.",
"improved_version": "Throughout the Results and Discussion, we explicitly reference the hypotheses: (1) that behavioral data can predict nonadherence, and (2) that models can be effective over extended durations, providing a clear thread of hypothesis testing.",
"explanation": "Explicit hypothesis tracking enhances logical progression and clarity.",
"location": "Throughout the paper",
"category": "hypothesis_tracking",
"focus": "hypothesis_tracking"
},
{
"original_text": "Figures and tables are detailed but could be better explained within the text.",
"improved_version": "In the Results section, we refer to Figure 6.5 to illustrate the trend of increasing AUC over weeks, and Table 6.1 to detail weekly model performance, providing interpretative commentary alongside the visuals.",
"explanation": "Enhances understanding by integrating visual data explanations into the narrative.",
"location": "Figures/Tables",
"category": "visual_integration",
"focus": "visual_element_integration"
},
{
"original_text": "The dense technical language may reduce reader engagement.",
"improved_version": "To maintain engagement, technical details are summarized in supplementary materials, while the main text emphasizes key findings and their implications in accessible language.",
"explanation": "Balancing technical detail with readability improves overall engagement.",
"location": "Throughout the document",
"category": "reader_engagement",
"focus": "reader_engagement"
},
{
"original_text": "The conclusion does not explicitly revisit the research questions.",
"improved_version": "The conclusion explicitly revisits the initial research questions, confirming that behavioral app engagement features can reliably predict nonadherence and discussing how these insights can inform future intervention strategies.",
"explanation": "Revisiting research questions provides narrative closure and reinforces the study's contributions.",
"location": "Conclusion",
"category": "conclusion_alignment",
"focus": "conclusion_alignment"
},
{
"original_text": "The paper would benefit from clearer signposting of the hypotheses and research aims throughout.",
"improved_version": "Throughout the manuscript, we explicitly state and revisit our research aims and hypotheses, ensuring each section contributes to addressing these core questions, thereby strengthening the narrative coherence.",
"explanation": "Explicit signposting guides the reader through the research narrative, enhancing clarity.",
"location": "Entire document",
"category": "hypothesis_tracking",
"focus": "hypothesis_tracking"
},
{
"original_text": "The extensive data presentation could be complemented with more interpretative commentary.",
"improved_version": "Alongside detailed tables and figures, we include interpretative summaries that highlight key trends and their implications for model performance and intervention design.",
"explanation": "Adding interpretative commentary makes data more accessible and meaningful, improving engagement.",
"location": "Results and Discussion",
"category": "evidence_integration",
"focus": "evidence_integration"
}
],
"detailed_feedback": {
"narrative_coherence": "The overall narrative is somewhat fragmented due to dense technical descriptions and limited explicit links between sections. Improving coherence involves clearer signposting, summarizing key points, and ensuring each part logically flows into the next, especially between data presentation and interpretation.",
"logical_progression": "The manuscript generally follows a logical sequence from background to methods, results, and discussion. However, some sections lack explicit connections to the initial hypotheses, which could be strengthened by explicitly stating how each result supports or refutes the research aims.",
"section_transitions": "Transitions between sections, particularly from Results to Discussion, are abrupt. Incorporating bridging sentences that summarize findings and preview their implications would improve flow and reader comprehension.",
"paragraph_organization": "Many paragraphs are overly dense, especially in Methods, which hampers readability. Breaking complex paragraphs into smaller, thematic units with clear topic sentences would enhance clarity and engagement.",
"topic_sentence_effectiveness": "Several paragraphs lack clear topic sentences, making it difficult for readers to grasp their main purpose quickly. Adding explicit topic sentences at the start of each paragraph would guide the reader effectively.",
"supporting_evidence_integration": "While the data are extensive, the narrative often lacks interpretative commentary that connects evidence to broader implications. Integrating discussion of how specific results support hypotheses or practical applications would strengthen the argument.",
"conclusion_alignment": "The conclusion summarizes findings but does not explicitly revisit the initial research questions or hypotheses, weakening the narrative closure. Explicitly linking conclusions back to original aims would improve coherence.",
"hypothesis_tracking": "The manuscript does not consistently track hypotheses throughout. Explicit references to hypotheses when presenting results would clarify how findings relate to initial research aims.",
"visual_element_integration": "Figures and tables are numerous but often lack direct references or explanations within the text. Explicitly referencing and interpreting visuals within the narrative would enhance their utility.",
"reader_engagement": "The dense technical language and extensive data presentation may reduce engagement. Balancing technical detail with accessible summaries and visual aids would improve overall readability and interest."
},
"summary": "This manuscript presents a comprehensive and technically detailed exploration of nonadherence prediction in mHealth interventions. Its strengths lie in extensive data analysis and model evaluation across multiple contexts. However, improvements in narrative coherence, explicit hypothesis tracking, section transitions, and visual integration are needed to enhance clarity and engagement. Structuring the content with clearer signposting, summarizing key points, and balancing technical detail with interpretative commentary will significantly strengthen the overall narrative flow and reader experience."
}

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Backup/V5_multi_agent2/results/W3_results.json
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@@ -1,202 +0,0 @@
{
"clarity_conciseness_score": 6,
"critical_remarks": [
{
"category": "language_simplicity",
"location": "Abstract",
"issue": "Use of complex phrases like 'the rising prevalence and economic burden of noncommunicable diseases (NCDs) present a significant challenge' may hinder quick understanding.",
"severity": "medium",
"impact": "Reduces immediate clarity for readers unfamiliar with technical language."
},
{
"category": "jargon",
"location": "Introduction",
"issue": "Terms like 'DiGA', 'PDT', 'stratified 10-fold cross-validation', and 'Tomek Links undersampling' are technical and may not be immediately clear to all readers.",
"severity": "high",
"impact": "Potentially alienates or confuses readers unfamiliar with specific technical terms."
},
{
"category": "wordiness",
"location": "Literature Review",
"issue": "Several sentences are overly long and contain redundant phrases, e.g., 'A growing body of evidence suggests that mHealth interventions can effectively support the prevention and management of NCDs by addressing modifiable risk factors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful use of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and hyperglycemia [19].'",
"severity": "high",
"impact": "Obscures key points, making the text harder to scan and understand quickly."
},
{
"category": "sentence_length",
"location": "Results",
"issue": "Many sentences, especially in the prediction results sections, are very long, containing multiple clauses and detailed data points.",
"severity": "high",
"impact": "Impairs readability and makes it difficult for readers to grasp main findings quickly."
},
{
"category": "paragraph_length",
"location": "Discussion",
"issue": "Some paragraphs, especially in the discussion, are densely packed with information, reducing clarity.",
"severity": "medium",
"impact": "Overwhelms readers, making it harder to follow key arguments."
},
{
"category": "voice",
"location": "Methodology",
"issue": "Use of passive voice in descriptions like 'Data was collected from...' and 'Models were trained...' can obscure agency and reduce engagement.",
"severity": "medium",
"impact": "Decreases immediacy and clarity of methodological descriptions."
},
{
"category": "redundancy",
"location": "Results",
"issue": "Repeatedly mentioning model performance metrics (e.g., AUC, accuracy, F1) with similar values across sections adds unnecessary repetition.",
"severity": "low",
"impact": "Reduces conciseness without adding new information."
},
{
"category": "ambiguity",
"location": "Introduction",
"issue": "Phrases like 'nonadherence can be accurately predicted' lack specificity about the degree or context of accuracy.",
"severity": "medium",
"impact": "Leaves some uncertainty about the practical significance of the findings."
},
{
"category": "readability",
"location": "Discussion",
"issue": "Use of technical terms and complex sentences throughout hampers ease of reading for a broad audience.",
"severity": "high",
"impact": "Limits accessibility and quick comprehension."
},
{
"category": "information_density",
"location": "Results & Discussion",
"issue": "High density of statistical data and model metrics in tables and text can overwhelm readers unfamiliar with statistical reporting.",
"severity": "medium",
"impact": "Reduces clarity of main findings and hampers quick understanding."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "The data collected by mHealth interventions raise the question: can we predict nonadherence using this data?",
"explanation": "Simplifies language and clarifies the research question, making it more direct and accessible.",
"location": "Abstract",
"category": "language_simplicity",
"focus": "language_simplicity"
},
{
"original_text": "Terms like 'DiGA', 'PDT', 'stratified 10-fold cross-validation', and 'Tomek Links undersampling' are technical and may not be immediately clear to all readers.",
"improved_version": "Terms such as 'DiGA' (digital health application), 'PDT' (digital therapeutic), and 'Tomek Links undersampling' (a data balancing technique) are technical; consider briefly defining them when first introduced.",
"explanation": "Provides clarity for readers unfamiliar with specific jargon, improving comprehension.",
"location": "Introduction",
"category": "jargon",
"focus": "jargon"
},
{
"original_text": "Several sentences are overly long and contain redundant phrases, e.g., 'A growing body of evidence suggests that mHealth interventions can effectively support the prevention and management of NCDs by addressing modifiable risk factors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful use of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and hyperglycemia [19].'",
"improved_version": "Evidence shows that mHealth interventions help prevent and manage NCDs by targeting risk factors like inactivity, poor diet, smoking, alcohol use, obesity, hypertension, and high blood sugar.",
"explanation": "Reduces wordiness and simplifies complex lists, making key points clearer and more concise.",
"location": "Literature Review",
"category": "wordiness",
"focus": "wordiness"
},
{
"original_text": "Many sentences, especially in the prediction results sections, are very long, containing multiple clauses and detailed data points.",
"improved_version": "Break long sentences into shorter ones. For example, instead of 'AUC ranged from 0.89 in Week 2 to 0.99 in Week 13,' write 'The AUC ranged from 0.89 in Week 2 to 0.99 in Week 13.'",
"explanation": "Shorter sentences improve readability and help readers grasp key findings more easily.",
"location": "Results",
"category": "sentence_length",
"focus": "sentence_length"
},
{
"original_text": "Some paragraphs, especially in the discussion, are densely packed with information, reducing clarity.",
"improved_version": "Divide lengthy paragraphs into smaller sections focused on single ideas. For example, separate discussion of model performance from implications for practice.",
"explanation": "Enhances readability by allowing readers to process one idea at a time.",
"location": "Discussion",
"category": "paragraph_length",
"focus": "paragraph_length"
},
{
"original_text": "Use of passive voice in descriptions like 'Data was collected from...' and 'Models were trained...' can obscure agency and reduce engagement.",
"improved_version": "Use active voice: 'We collected data from...' and 'We trained the models...'.",
"explanation": "Active voice makes sentences clearer and more direct, increasing engagement.",
"location": "Methodology",
"category": "active_passive_voice",
"focus": "active_passive_voice"
},
{
"original_text": "Repeatedly mentioning model performance metrics (e.g., AUC, accuracy, F1) with similar values across sections adds unnecessary repetition.",
"improved_version": "Summarize key performance metrics once in a table or summary paragraph instead of repeating similar details in multiple sections.",
"explanation": "Reduces redundancy, making the text more concise and focused.",
"location": "Results",
"category": "redundancy",
"focus": "redundancy"
},
{
"original_text": "Phrases like 'nonadherence can be accurately predicted' lack specificity about the degree or context of accuracy.",
"improved_version": "Nonadherence was predicted with over 90% accuracy, indicating high reliability in our models.",
"explanation": "Provides concrete figures, clarifying the level of accuracy and strengthening the statement.",
"location": "Introduction",
"category": "ambiguity",
"focus": "ambiguity"
},
{
"original_text": "Use of technical terms and complex sentences throughout hampers ease of reading for a broad audience.",
"improved_version": "Simplify technical language where possible and use shorter sentences to improve overall readability for diverse readers.",
"explanation": "Enhances accessibility, allowing a wider audience to understand key points more easily.",
"location": "Discussion",
"category": "readability",
"focus": "readability"
},
{
"original_text": "High density of statistical data and model metrics in tables and text can overwhelm readers unfamiliar with statistical reporting.",
"improved_version": "Include only the most relevant statistics in the main text; move detailed tables to appendices or supplementary materials.",
"explanation": "Reduces cognitive load and helps highlight key findings without overwhelming the reader.",
"location": "Results & Discussion",
"category": "information_density",
"focus": "information_density"
},
{
"original_text": "Many sentences are very long and contain multiple clauses, making them difficult to follow.",
"improved_version": "Shorten complex sentences into simpler, standalone sentences. For example, 'Models achieved high performance. They predicted nonadherence accurately.'",
"explanation": "Improves clarity and ease of reading by reducing sentence complexity.",
"location": "Results",
"category": "sentence_length",
"focus": "sentence_length"
},
{
"original_text": "Some technical descriptions, such as 'stratified 10-fold cross-validation,' could benefit from brief explanations for clarity.",
"improved_version": "Use: 'We used stratified 10-fold cross-validation, a method that splits data into 10 parts while maintaining class proportions, to evaluate model performance.'",
"explanation": "Clarifies technical terms, making methods transparent to non-expert readers.",
"location": "Methodology",
"category": "jargon",
"focus": "jargon"
},
{
"original_text": "The dense presentation of data and statistical results can be overwhelming.",
"improved_version": "Summarize key findings in text and present detailed data in well-organized tables or figures, with clear labels and legends.",
"explanation": "Enhances readability and helps readers focus on main insights without distraction.",
"location": "Results",
"category": "information_density",
"focus": "information_density"
},
{
"original_text": "Some sections contain redundant information, such as repeatedly stating model performance metrics with similar values.",
"improved_version": "Consolidate performance metrics into summary statements, e.g., 'Models consistently achieved AUCs above 0.87, indicating strong predictive ability.'",
"explanation": "Reduces repetition, making the narrative more concise and focused.",
"location": "Results & Discussion",
"category": "redundancy",
"focus": "redundancy"
}
],
"detailed_feedback": {
"language_simplicity": "The manuscript employs complex language and lengthy sentences, which can hinder quick understanding. Simplifying vocabulary and breaking long sentences into shorter, clearer ones will improve accessibility for a broader audience.",
"jargon_usage": "While technical terms are necessary for precision, many are introduced without explanation. Providing brief definitions or explanations upon first use will help non-expert readers grasp the concepts more easily.",
"wordiness": "Several sections contain verbose sentences with redundant phrases, which obscure key messages. Eliminating unnecessary words and focusing on core ideas will enhance clarity.",
"sentence_length": "Long, multi-clause sentences are prevalent, especially in data-heavy sections. Shortening sentences or splitting them into two will improve readability and comprehension.",
"paragraph_length": "Some paragraphs are densely packed with information, making it difficult to follow. Dividing lengthy paragraphs into smaller, focused sections will aid reader engagement.",
"active_passive_voice": "The frequent use of passive voice reduces immediacy and clarity. Rephrasing sentences to active voice will make descriptions more direct and engaging.",
"redundancy": "Repeated presentation of similar performance metrics and detailed data points can clutter the text. Summarizing key results and relocating detailed data to appendices will streamline the narrative.",
"ambiguity": "Certain statements lack specificity, such as 'nonadherence can be accurately predicted,' without quantifying accuracy. Including concrete metrics will clarify the strength of the findings.",
"readability": "Technical complexity and dense data presentation hinder ease of reading. Simplifying language, reducing jargon, and organizing data visually will improve overall readability.",
"information_density": "The manuscript contains a high volume of detailed statistical data, which can overwhelm readers. Focusing on main findings and relegating detailed tables to supplementary sections will enhance clarity."
},
"summary": "Overall, the manuscript presents valuable findings on predicting nonadherence in mHealth interventions but would benefit from clearer, more concise language, reduced technical jargon, and better organization of data. Shortening sentences, clarifying technical terms, and dividing dense paragraphs will significantly improve readability and comprehension for a diverse audience."
}

178
Backup/V5_multi_agent2/results/W4_results.json
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@@ -1,178 +0,0 @@
{
"terminology_consistency_score": 7,
"critical_remarks": [
{
"category": "term_usage",
"location": "Abstract, Paragraph 1",
"issue": "The term 'nonadherence' is introduced and used throughout, but sometimes it is contrasted with 'churn' without clear differentiation or consistent usage, leading to potential confusion.",
"severity": "medium",
"impact": "Inconsistent use of 'nonadherence' and 'churn' can cause ambiguity in understanding the scope of the study and the specific behaviors being predicted."
},
{
"category": "notation",
"location": "Equations and Tables, e.g., Table 6.1",
"issue": "Performance metrics such as AUC, accuracy, F1-score are presented with inconsistent formatting and sometimes without units or clear explanation, e.g., SD values are sometimes in parentheses, sometimes not.",
"severity": "low",
"impact": "Inconsistent notation reduces clarity and may cause misinterpretation of statistical results."
},
{
"category": "acronyms",
"location": "Introduction and Methods",
"issue": "Acronyms like NCDs, mHealth, DiGA, SHI, PHI are used before being explicitly defined, or their definitions are scattered, leading to potential confusion for readers unfamiliar with these terms.",
"severity": "high",
"impact": "Lack of clear, early definitions of acronyms hampers reader comprehension and consistency in terminology."
},
{
"category": "variable_naming",
"location": "Results and Tables, e.g., Table 6.1",
"issue": "Variables such as 'nonadherent users', 'churned users', 'active users' are used, but sometimes the criteria for these categories (e.g., thresholds) are described differently in different sections, risking inconsistency.",
"severity": "medium",
"impact": "Inconsistent variable definitions can lead to confusion about what exactly is being measured and predicted."
},
{
"category": "unit_notation",
"location": "Methodology, Descriptive Statistics",
"issue": "Time units such as days, weeks, and months are used variably with inconsistent formatting (e.g., 'Days 1-7', 'Week 1', 'Month 2') without uniform notation or clarification.",
"severity": "low",
"impact": "Inconsistent notation of time units can cause misinterpretation of the temporal scope of data and analysis."
},
{
"category": "abbreviations",
"location": "Throughout the document",
"issue": "Some abbreviations like 'SD', 'IQR', 'n' are used without initial expansion or clear explanation, and sometimes the same abbreviation is used for different concepts.",
"severity": "medium",
"impact": "Inconsistent abbreviation usage reduces clarity and may lead to misunderstandings."
},
{
"category": "technical_terms",
"location": "Discussion, Paragraph 1",
"issue": "Terms such as 'predictive performance', 'classification', 'model accuracy' are used interchangeably without precise definitions or distinctions, which could cause confusion.",
"severity": "low",
"impact": "Ambiguous technical terminology can impair precise understanding of the results."
},
{
"category": "field_terminology",
"location": "Introduction, Paragraph 2",
"issue": "Terms like 'adherence', 'nonadherence', 'churn', 'dropout' are used, but their definitions vary slightly across literature and are sometimes used interchangeably without clarification.",
"severity": "high",
"impact": "Inconsistent field-specific terminology affects the clarity of the conceptual framework."
},
{
"category": "cross_references",
"location": "Throughout the document",
"issue": "References to figures, tables, and appendices (e.g., 'Figure 6.1', 'Appendix 6.2') are sometimes inconsistent in formatting or missing, which can hinder navigation.",
"severity": "low",
"impact": "Inconsistent cross-referencing impacts document usability."
},
{
"category": "definition_consistency",
"location": "Methodology, Dataset descriptions",
"issue": "Definitions of adherence (e.g., 'completing eight exercises per week') are provided, but similar definitions for 'churn' or 'nonadherence' are sometimes described differently in various sections, risking inconsistency.",
"severity": "medium",
"impact": "Inconsistent definitions compromise the clarity of outcome measures."
}
],
"improvement_suggestions": [
{
"original_text": "nonadherence was measured weekly, defined as completing eight or more exercises per week.",
"improved_version": "Nonadherence was measured weekly, defined as completing fewer than eight exercises per week.",
"explanation": "Clarifies the threshold for nonadherence, ensuring consistent understanding of the criterion.",
"location": "Methodology, Dataset descriptions",
"category": "term_usage",
"focus": "term_usage"
},
{
"original_text": "churn (users\u2019 last login within program duration)",
"improved_version": "churn, defined as the user's last login within the program duration, indicating complete discontinuation.",
"explanation": "Provides a clear, consistent definition of churn aligned with the literature and the context, reducing ambiguity.",
"location": "Introduction, Methods",
"category": "field_terminology",
"focus": "definitions"
},
{
"original_text": "The performance metrics such as AUC, accuracy, F1-score are presented with inconsistent formatting.",
"improved_version": "Performance metrics such as Area Under the Curve (AUC), accuracy, and F1-score are consistently formatted, with abbreviations defined at first use and values presented with standard decimal notation.",
"explanation": "Enhances clarity and uniformity in reporting statistical results.",
"location": "Results, Tables",
"category": "notation",
"focus": "notation"
},
{
"original_text": "Acronyms like NCDs, mHealth, DiGA, SHI, PHI are used before being explicitly defined.",
"improved_version": "Define all acronyms upon first mention, e.g., 'noncommunicable diseases (NCDs)', 'mobile health (mHealth)', 'Digital Healthcare Act (DiGA)', 'Statutory Health Insurance (SHI)', 'Private Health Insurance (PHI)'.",
"explanation": "Ensures all readers understand the acronyms from the first occurrence, improving clarity.",
"location": "Introduction, Methods",
"category": "acronyms",
"focus": "definition"
},
{
"original_text": "Variables such as 'nonadherent users', 'churned users' are used with thresholds described in different sections.",
"improved_version": "Standardize variable definitions throughout the manuscript, explicitly stating thresholds (e.g., 'nonadherent users: those completing fewer than 8 exercises per week') and maintaining consistent terminology.",
"explanation": "Prevents confusion by ensuring uniform understanding of key variables.",
"location": "Results, Discussion",
"category": "variable_naming",
"focus": "definition"
},
{
"original_text": "Time units such as days, weeks, and months are used variably with inconsistent formatting.",
"improved_version": "Adopt a uniform format for time units, e.g., always write 'Days 1-7', 'Week 1', 'Month 2', and clarify the time frame when first introduced.",
"explanation": "Reduces ambiguity and improves readability regarding temporal data.",
"location": "Methodology, Results",
"category": "unit_notation",
"focus": "notation"
},
{
"original_text": "Some abbreviations like 'SD', 'IQR', 'n' are used without initial expansion.",
"improved_version": "Initially define abbreviations such as 'Standard Deviation (SD)', 'Interquartile Range (IQR)', and 'sample size (n)' at their first appearance.",
"explanation": "Ensures clarity for readers unfamiliar with statistical abbreviations.",
"location": "Appendix, Descriptive statistics",
"category": "abbreviations",
"focus": "definition"
},
{
"original_text": "Terms like 'predictive performance', 'classification', 'model accuracy' are used interchangeably.",
"improved_version": "Use precise terminology: 'predictive performance' for overall assessment, 'classification accuracy' for specific metric, and clearly distinguish between 'model accuracy', 'precision', 'recall', etc., with definitions if necessary.",
"explanation": "Reduces ambiguity by clarifying technical terms and their specific meanings.",
"location": "Discussion, Results",
"category": "technical_terms",
"focus": "term_usage"
},
{
"original_text": "Terms like 'adherence', 'nonadherence', 'churn', 'dropout' are used interchangeably.",
"improved_version": "Differentiate clearly: 'adherence' refers to following the prescribed use, 'nonadherence' to failing to meet the threshold, 'churn' to complete discontinuation, and 'dropout' as a specific form of churn, with consistent definitions provided early.",
"explanation": "Ensures precise understanding of each concept and their relationships.",
"location": "Introduction, Methods",
"category": "field_terminology",
"focus": "definitions"
},
{
"original_text": "References to figures and tables (e.g., 'Figure 6.1') are sometimes inconsistent.",
"improved_version": "Ensure all cross-references follow a uniform format, e.g., 'Figure 6.1', 'Table 6.1', and verify that all references are correctly numbered and linked.",
"explanation": "Improves navigation and consistency in referencing visual aids.",
"location": "Throughout the document",
"category": "cross_references",
"focus": "formatting"
},
{
"original_text": "Definitions of adherence and nonadherence are scattered and sometimes inconsistent.",
"improved_version": "Provide a comprehensive, consistent definition of adherence and nonadherence early in the Methods section, and use these definitions uniformly throughout the manuscript.",
"explanation": "Maintains clarity and consistency in outcome measures and key concepts.",
"location": "Methods, Results, Discussion",
"category": "definition_consistency",
"focus": "definitions"
}
],
"detailed_feedback": {
"term_usage_consistency": "The manuscript generally uses 'nonadherence' and 'churn' appropriately, but occasionally conflates or contrasts them without clear distinction. Consistent terminology should be maintained, with explicit definitions and consistent application across sections to avoid confusion.",
"notation_consistency": "Statistical metrics such as AUC, accuracy, and F1-score are presented with parentheses or without, and sometimes with SD values. Standardizing formatting\u2014such as always writing 'mean (SD)'\u2014will improve clarity. Also, ensure all units are explicitly stated and consistently formatted.",
"acronym_usage": "Many acronyms are introduced without initial expansion, which can hinder understanding for readers unfamiliar with the terms. All acronyms should be spelled out at first use, with the abbreviation in parentheses, and used consistently thereafter.",
"variable_naming_consistency": "Variables like 'nonadherent users' and 'churned users' are sometimes defined with thresholds in one section but not in others. Establish clear, uniform definitions early and apply them consistently throughout to avoid ambiguity.",
"unit_notation_consistency": "Time-related data are expressed as 'Days 1-7', 'Week 1', 'Month 2' with varying formats. Adopting a uniform notation and explicitly defining the time frames at first mention will enhance clarity.",
"abbreviation_consistency": "Abbreviations such as 'SD', 'IQR', 'n' are used without initial definitions or inconsistent formatting. All abbreviations should be defined upon first mention and used uniformly.",
"technical_term_consistency": "Terms like 'predictive performance' and 'classification' are used broadly without precise definitions. Clarify these terms when first introduced and maintain consistent usage to improve technical clarity.",
"field_terminology": "Key concepts like 'adherence', 'nonadherence', 'churn', and 'dropout' are sometimes used interchangeably or without clear distinctions. Explicit, consistent definitions aligned with the literature will strengthen conceptual clarity.",
"cross_reference_consistency": "References to figures, tables, and appendices should follow a uniform format and be checked for correctness. Proper cross-referencing improves navigation and reduces confusion.",
"definition_consistency": "Definitions of core concepts such as 'adherence' and 'nonadherence' are scattered and sometimes inconsistent. Providing comprehensive, early, and uniform definitions will improve overall clarity and understanding."
},
"summary": "Overall, the manuscript demonstrates good use of terminology but would benefit from systematic standardization across all sections. Clarifying definitions, maintaining consistent notation, and explicitly defining acronyms and technical terms will significantly enhance clarity, coherence, and the scientific rigor of the presentation."
}

194
Backup/V5_multi_agent2/results/W5_results.json
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@@ -1,194 +0,0 @@
{
"inclusive_language_score": 4,
"critical_remarks": [
{
"category": "gender_neutrality",
"location": "Abstract",
"issue": "The abstract primarily uses gendered pronouns like 'users' without gender-specific language, but the presentation is neutral; however, the gender distribution is reported with '68.3% female' and '31.6% male,' which could be more inclusive by emphasizing diversity beyond binary categories.",
"severity": "low",
"impact": "Limited inclusivity in gender reporting; could overlook non-binary identities."
},
{
"category": "cultural_sensitivity",
"location": "Introduction",
"issue": "The description of health systems and interventions is Germany-centric, with limited acknowledgment of diverse global contexts, which may reduce cultural sensitivity and inclusivity for international audiences.",
"severity": "medium",
"impact": "Restricts the perceived applicability of findings across different cultural and healthcare settings."
},
{
"category": "age_terminology",
"location": "Participant descriptions (Vivira and Manoa datasets)",
"issue": "Age categories are described with ranges like '18\u201335 years' and 'over 75 years,' which are appropriate, but the language could be more inclusive by explicitly stating that age is a variable and avoiding implying age-related stereotypes.",
"severity": "low",
"impact": "Minimal, but more explicit acknowledgment of age diversity would improve inclusivity."
},
{
"category": "disability_inclusion",
"location": "Methodology",
"issue": "The language assumes all users are able-bodied and does not explicitly consider users with disabilities or impairments, which could marginalize those with disabilities.",
"severity": "high",
"impact": "Excludes or unintentionally marginalizes users with disabilities, reducing the inclusivity of the research."
},
{
"category": "socioeconomic_sensitivity",
"location": "Introduction and methodology",
"issue": "The socioeconomic background of users is not discussed; the focus on smartphone access and healthcare systems may overlook socioeconomic barriers faced by marginalized populations.",
"severity": "medium",
"impact": "Limits understanding of how socioeconomic factors influence adherence and engagement, reducing overall inclusivity."
},
{
"category": "geographic_inclusivity",
"location": "Introduction and datasets description",
"issue": "The study is Germany-centric, with references to German healthcare systems and regulations, which may limit applicability and inclusivity for populations in other geographic regions.",
"severity": "medium",
"impact": "May reduce relevance for international or non-European populations, limiting global inclusivity."
},
{
"category": "professional_titles",
"location": "Methodology and author contributions",
"issue": "The text uses titles like 'Jakob R,' 'Benning L,' etc., but does not specify professional titles or roles, which could be more respectful and precise.",
"severity": "low",
"impact": "Minor; clearer professional titles could enhance clarity and respect."
},
{
"category": "stereotype_avoidance",
"location": "Introduction and discussion",
"issue": "The language does not reinforce stereotypes; however, the focus on age and gender distributions without contextualizing diversity may inadvertently reinforce stereotypes about certain groups' engagement levels.",
"severity": "low",
"impact": "Minimal, but explicit acknowledgment of diversity would prevent stereotypes."
},
{
"category": "identity_language",
"location": "Participant descriptions",
"issue": "Participants are described with binary gender categories ('female,' 'male') and a small 'non-binary' label, but the language could be more inclusive by emphasizing that gender identity is diverse and fluid.",
"severity": "medium",
"impact": "Reduces recognition of gender diversity beyond binary categories."
},
{
"category": "historical_context",
"location": "Introduction and discussion",
"issue": "The discussion of digital health systems is historical and Germany-specific, with limited acknowledgment of global developments or the evolving nature of digital health, which could be more inclusive of diverse historical contexts.",
"severity": "low",
"impact": "Limits the understanding of global and historical diversity in digital health evolution."
}
],
"improvement_suggestions": [
{
"original_text": "68.3% were female, 31.6% were male, and 0.1% non-binary.",
"improved_version": "68.3% identified as women, 31.6% as men, and 0.1% as non-binary or other gender identities.",
"explanation": "Using 'identified as' and inclusive categories acknowledges gender diversity beyond binary labels, promoting greater inclusivity.",
"location": "Participant descriptions (Vivira dataset)",
"category": "gender_neutrality",
"focus": "gender_neutrality"
},
{
"original_text": "The description of health systems and interventions is Germany-centric.",
"improved_version": "While this study focuses on the German healthcare context, the methodologies and findings may be adaptable to diverse international healthcare systems, emphasizing the importance of cultural and systemic differences.",
"explanation": "Acknowledging the context's specificity while highlighting potential for broader application enhances cultural sensitivity and inclusivity.",
"location": "Introduction",
"category": "cultural_sensitivity",
"focus": "cultural_sensitivity"
},
{
"original_text": "Participants are described with age ranges such as 18\u201335 years.",
"improved_version": "Participants' ages are categorized into ranges such as 18\u201335 years, with an emphasis that age is a variable that encompasses diverse life stages and experiences, avoiding stereotypes about engagement or health behaviors based on age.",
"explanation": "Explicitly framing age as a broad, inclusive variable helps prevent age-related stereotypes and promotes age diversity acknowledgment.",
"location": "Participant descriptions",
"category": "age_terminology",
"focus": "age_terminology"
},
{
"original_text": "The language assumes all users are able-bodied.",
"improved_version": "Future research should consider including users with disabilities or impairments, ensuring accessibility features are evaluated and that the language and design are inclusive of diverse physical abilities.",
"explanation": "Explicitly recognizing users with disabilities promotes inclusivity and encourages accessible design and research practices.",
"location": "Methodology",
"category": "disability_inclusion",
"focus": "disability_inclusion"
},
{
"original_text": "The socioeconomic background of users is not discussed.",
"improved_version": "Future studies should explore socioeconomic factors influencing engagement, such as access to smartphones, internet connectivity, and health literacy, to better understand barriers faced by marginalized populations.",
"explanation": "Addressing socioeconomic barriers broadens the inclusivity of the research and highlights disparities that may affect adherence.",
"location": "Introduction and methodology",
"category": "socioeconomic_sensitivity",
"focus": "socioeconomic_sensitivity"
},
{
"original_text": "The study is Germany-centric, with references to German healthcare systems.",
"improved_version": "While this study is based on the German healthcare context, the methodologies and insights are intended to inform international applications, with adaptations made for different healthcare systems and cultural settings.",
"explanation": "Explicitly stating potential for international relevance promotes geographic inclusivity and acknowledges diversity in healthcare contexts.",
"location": "Introduction",
"category": "geographic_inclusivity",
"focus": "geographic_inclusivity"
},
{
"original_text": "Author contributions list names without titles.",
"improved_version": "Author contributions should include professional titles or roles (e.g., researcher, clinician, data scientist) to clarify expertise and foster respect.",
"explanation": "Using professional titles clarifies roles and promotes respectful acknowledgment of diverse expertise.",
"location": "Author contributions",
"category": "professional_titles",
"focus": "professional_titles"
},
{
"original_text": "Participants are described with binary gender categories.",
"improved_version": "Participants' gender identities are acknowledged as diverse, with options beyond binary categories, such as 'woman,' 'man,' 'non-binary,' 'prefer to self-describe,' or 'prefer not to say.'",
"explanation": "This approach respects gender diversity and reduces binary stereotypes, fostering inclusivity.",
"location": "Participant descriptions",
"category": "stereotypes",
"focus": "stereotypes"
},
{
"original_text": "Participants are described as 'users' without explicit mention of their gender identity.",
"improved_version": "Participants are described as 'individuals' or 'people' to emphasize personhood over labels, and gender identity is acknowledged as diverse and fluid where relevant.",
"explanation": "Using person-centered language avoids stereotypes and emphasizes individual diversity.",
"location": "Participant descriptions",
"category": "identity_language",
"focus": "identity_language"
},
{
"original_text": "Descriptions of health systems focus on Germany-specific policies.",
"improved_version": "The discussion of health systems includes a brief overview of global developments in digital health, recognizing the evolving and diverse nature of digital therapeutics worldwide.",
"explanation": "This broadens the historical and contextual perspective, making the content more globally inclusive.",
"location": "Introduction and discussion",
"category": "historical_context",
"focus": "historical_context"
},
{
"original_text": "The language in the discussion emphasizes technological and healthcare system specifics without addressing cultural diversity.",
"improved_version": "Future research should consider cultural differences in health behaviors, technology acceptance, and healthcare access, ensuring that models and interventions are adaptable across diverse populations.",
"explanation": "Acknowledging cultural diversity enhances the cultural sensitivity and global applicability of the research.",
"location": "Discussion",
"category": "cultural_sensitivity",
"focus": "cultural_sensitivity"
},
{
"original_text": "The participant demographics are primarily binary gender categories with minimal diversity acknowledgment.",
"improved_version": "Participant demographics should include a broader spectrum of gender identities, cultural backgrounds, and socioeconomic statuses, with explicit efforts to recruit diverse populations where possible.",
"explanation": "This promotes inclusivity and reduces marginalization of underrepresented groups.",
"location": "Participant descriptions",
"category": "gender_neutrality",
"focus": "gender_neutrality"
},
{
"original_text": "The language assumes all participants are able to engage with mobile technology equally.",
"improved_version": "Researchers should recognize and address barriers faced by users with disabilities or limited access to technology, designing interventions that are accessible and inclusive for all users.",
"explanation": "This promotes disability inclusion and broadens the reach of interventions.",
"location": "Methodology",
"category": "disability_inclusion",
"focus": "disability_inclusion"
}
],
"detailed_feedback": {
"gender_neutral_language": "The current text often reports gender data in binary terms, which can unintentionally reinforce gender stereotypes or exclude non-binary identities. Using inclusive language such as 'individuals identifying as women, men, non-binary, or other gender identities' respects gender diversity and promotes inclusivity. Additionally, emphasizing that gender is a spectrum and that data collection methods can be expanded to include more options enhances the research's sensitivity.",
"cultural_sensitivity": "The focus on German healthcare systems and regulations limits the cultural scope of the research. To improve cultural sensitivity, the manuscript should acknowledge that health behaviors, system structures, and acceptance of digital health vary globally. Including a discussion on how findings could be adapted or interpreted in different cultural contexts fosters inclusivity of diverse populations and international applicability.",
"age_appropriate_terminology": "The age ranges used are appropriate; however, the language could be more inclusive by explicitly stating that age is a variable representing diverse life stages and experiences. Avoiding implicit stereotypes about engagement or health behaviors based on age helps foster a more inclusive perspective. For example, recognizing that older adults may have different needs and capacities encourages broader participation.",
"disability_inclusive_language": "The current description assumes all users are able-bodied and does not explicitly consider users with disabilities. Incorporating language that emphasizes accessibility and invites inclusion of users with impairments\u2014such as 'designed to be accessible for users with diverse physical abilities'\u2014would promote disability inclusion and ensure the research considers a broader user base.",
"socioeconomic_sensitivity": "The manuscript does not address socioeconomic barriers, such as access to smartphones, internet, or digital literacy, which can significantly impact engagement. Including a discussion on how socioeconomic factors influence adherence and how interventions can be tailored or made accessible to marginalized groups enhances socioeconomic sensitivity and equity.",
"geographic_inclusivity": "The study's focus on Germany limits its geographic inclusivity. To broaden this, the authors should acknowledge that healthcare systems, cultural attitudes, and technology acceptance differ worldwide. Suggesting that future research validate models in diverse settings promotes a more inclusive and globally relevant approach.",
"professional_titles": "Author contributions list names without titles or roles. Including professional titles (e.g., 'Dr.', 'Professor', 'Data Scientist') clarifies expertise and fosters respect for diverse roles, enhancing transparency and inclusivity in acknowledgment.",
"stereotype_avoidance": "While the language does not overtly reinforce stereotypes, the demographic reporting could inadvertently imply stereotypes about engagement levels based on age or gender. Explicitly emphasizing diversity and avoiding assumptions about behavior based on demographic categories helps prevent stereotypes.",
"identity_language": "Participants are described with binary gender labels and a small 'non-binary' mention. Using inclusive language such as 'participants' with self-identified gender labels or open options like 'prefer to self-describe' respects gender fluidity and diversity, promoting a more inclusive approach.",
"historical_context": "The focus on German digital health policies and systems is specific and may overlook the broader evolution of digital therapeutics globally. Including a brief overview of international developments and acknowledging the dynamic, evolving nature of digital health ensures sensitivity to different historical and cultural contexts, making the research more inclusive."
},
"summary": "Overall, the manuscript demonstrates a generally neutral and objective tone but could significantly benefit from more explicit inclusive language and broader cultural, gender, and disability considerations. Addressing these areas will enhance the research's accessibility, relevance, and respect for diverse populations, aligning with best practices in inclusive and unbiased scientific communication. Implementing specific suggestions across sections will improve the manuscript's sensitivity and global applicability, fostering a more equitable scientific discourse."
}

242
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@@ -1,242 +0,0 @@
{
"citation_formatting_score": 4,
"critical_remarks": [
{
"category": "in_text_format",
"location": "Introduction paragraph, lines 16-44",
"issue": "In-text citations are inconsistently formatted; some are bracketed with brackets and numbers, others are embedded with author names and years, and some lack proper punctuation or formatting.",
"severity": "high",
"impact": "Reduces readability and undermines the scholarly credibility of the manuscript, making it difficult to verify sources and follow citation conventions."
},
{
"category": "reference_format",
"location": "Bibliography section",
"issue": "References are inconsistently formatted: some entries include DOIs, URLs, and journal details properly, others omit critical information like volume, issue, or page numbers, and some have inconsistent punctuation and line breaks.",
"severity": "high",
"impact": "Impairs the professional appearance and hampers accurate indexing, retrieval, and verification of sources."
},
{
"category": "style_consistency",
"location": "Throughout the references list",
"issue": "Multiple citation styles are used interchangeably, including numbered brackets, author-year formats, and varying punctuation styles, leading to a lack of uniformity.",
"severity": "high",
"impact": "Creates confusion and diminishes the manuscript's scholarly rigor, potentially violating journal submission guidelines."
},
{
"category": "reference_completeness",
"location": "Multiple references, e.g., [1], [2], [3]",
"issue": "Several references lack complete bibliographic details, such as missing volume, issue, page numbers, or publication year, especially for online-only sources.",
"severity": "high",
"impact": "Prevents proper citation tracking and reduces the credibility of the references."
},
{
"category": "doi_format",
"location": "References, e.g., [1], [2], [3]",
"issue": "DOIs are inconsistently formatted; some are complete URLs (https://doi.org/...), others are just numeric, and some are missing entirely.",
"severity": "medium",
"impact": "Hinders direct access to sources and violates standard DOI formatting guidelines."
},
{
"category": "author_name_formatting",
"location": "References, e.g., [1], [2], [3]",
"issue": "Author names are inconsistently formatted; some use full names, others initials, and some have inconsistent ordering or punctuation.",
"severity": "medium",
"impact": "Reduces clarity and professionalism, complicates author attribution, and affects citation indexing."
},
{
"category": "publication_date_formatting",
"location": "References, e.g., [1], [2], [3]",
"issue": "Publication dates are inconsistently formatted; some include month and year, others only year, and some have extraneous information.",
"severity": "medium",
"impact": "Impairs chronological clarity and consistency across references."
},
{
"category": "journal_format",
"location": "References, e.g., [1], [2], [3]",
"issue": "Journal names are variably formatted; some are italicized, others are plain text, and some abbreviations are inconsistent or missing.",
"severity": "medium",
"impact": "Affects professional presentation and adherence to journal style guides."
},
{
"category": "volume_format",
"location": "References, e.g., [1], [2], [3]",
"issue": "Volume, issue, and page numbers are inconsistently formatted; some include parentheses, others omit issue numbers, and page ranges are sometimes missing or improperly formatted.",
"severity": "medium",
"impact": "Reduces clarity and hampers accurate source identification."
},
{
"category": "cross_reference",
"location": "Throughout the manuscript",
"issue": "In-text citations do not consistently match reference list entries; some citations are missing, and numbering or author-year references are mismatched or inconsistent.",
"severity": "high",
"impact": "Undermines the integrity of scholarly referencing and impairs verification of sources."
}
],
"improvement_suggestions": [
{
"original_text": "In: (2025) (In preparation)",
"improved_version": "In: Jakob R, Benning L, S\u00e9vin CM, Von Wangenheim F, Fleisch E, Kowatsch T. (2025). Predicting Nonadherence to Mobile Health Interventions. (In preparation).",
"explanation": "Standardizes the in-text citation format for a forthcoming publication, aligning with common scholarly conventions.",
"location": "Reference entry for the planned publication",
"category": "references",
"focus": "reference_format"
},
{
"original_text": "[16, 26, 44, 64]",
"improved_version": "(Author et al., 2016; Author et al., 2020; Author et al., 2022; Author et al., 2015)",
"explanation": "Converts numeric citations to author-year format for clarity and consistency, especially in-text, aligning with common styles like APA or Vancouver with author-year.",
"location": "Introduction paragraph, lines 16-44",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[1]",
"improved_version": "(Alessa et al., 2018)",
"explanation": "Provides author name and year for clarity, making it easier for readers to identify sources without cross-referencing numbers.",
"location": "Introduction, paragraph 2, lines 16-44",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[54, 55]",
"improved_version": "Bl\u00fcmel et al., 2020; Teepe et al., 2022",
"explanation": "Replaces numeric references with author-year citations for consistency and readability.",
"location": "Introduction, paragraph 2, lines 16-44",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "https://doi.org/10.2196/10723",
"improved_version": "https://doi.org/10.2196/10723",
"explanation": "Ensure all DOIs are formatted as complete URLs starting with https://doi.org/ for uniformity and easy access.",
"location": "References, e.g., [1]",
"category": "doi_format",
"focus": "doi_format"
},
{
"original_text": "[2]",
"improved_version": "Ganju et al., 2020",
"explanation": "Replace numeric citation with author-year format for clarity and consistency in the references list.",
"location": "Throughout the manuscript, e.g., in-text citations",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[3]",
"improved_version": "Beger et al., 2023",
"explanation": "Standardizes citation style to author-year for consistency and easier source identification.",
"location": "Throughout the manuscript",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[4]",
"improved_version": "Bl\u00fcmel et al., 2020",
"explanation": "Provides full author names and year, aligning with common citation styles and improving clarity.",
"location": "Introduction, paragraph 2",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[5]",
"improved_version": "Bricker et al., 2023",
"explanation": "Ensures consistent author-year citation style for all references, enhancing uniformity.",
"location": "Results section, paragraph 3",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[6]",
"improved_version": "Donkin et al., 2011",
"explanation": "Aligns with the author-year citation style, facilitating easier cross-referencing and readability.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[7]",
"improved_version": "Drachen et al., 2016",
"explanation": "Standardizes citation style, improving consistency across the manuscript.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[8]",
"improved_version": "Ernsting et al., 2017",
"explanation": "Provides clarity and uniformity in referencing, aiding reader comprehension.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[9]",
"improved_version": "Eysenbach, 2005",
"explanation": "Ensures consistent author-year citation style, improving scholarly presentation.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[10]",
"improved_version": "Firth et al., 2017",
"explanation": "Aligns with the author-year citation style, aiding clarity and uniformity.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[11]",
"improved_version": "Fleming et al., 2018",
"explanation": "Provides consistent citation style, enhancing professional appearance.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[12]",
"improved_version": "Fowler et al., 2016",
"explanation": "Standardizes citation style for clarity and consistency.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[13]",
"improved_version": "F\u00fcrstenau et al., 2023",
"explanation": "Ensures uniform author-year citation style, improving readability.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[14]",
"improved_version": "Ganju et al., 2020",
"explanation": "Provides clarity and consistency in citations, aiding verification.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
},
{
"original_text": "[15]",
"improved_version": "Geiler et al., 2022",
"explanation": "Standardizes citation style for uniformity and professionalism.",
"location": "Discussion, paragraph 4",
"category": "in_text_format",
"focus": "in_text_format"
}
],
"detailed_feedback": {
"in_text_citation_format": "The manuscript employs a mixture of numeric bracketed citations and author-year formats, leading to inconsistency. For clarity and adherence to common academic standards, all in-text citations should follow a uniform style, preferably author-year, especially in the main text. This enhances readability and allows easier cross-referencing with the reference list.",
"reference_list_format": "The reference list exhibits inconsistent formatting: some entries include full journal titles, volume, issue, page numbers, and DOIs, while others omit these details. Standardizing the format according to a recognized style guide (e.g., APA, Vancouver) and ensuring all references contain complete bibliographic information will improve professionalism and facilitate source verification.",
"citation_style_consistency": "Currently, the manuscript switches between numbered citations and author-year formats, which can confuse readers and violate journal guidelines. Adopting a single, consistent citation style throughout the document is essential for scholarly rigor.",
"reference_completeness": "Many references lack complete details such as volume, issue, page ranges, or publication dates. Ensuring each reference includes all necessary bibliographic elements will improve accuracy and traceability.",
"doi_url_formatting": "DOIs are inconsistently formatted; some are presented as URLs (https://doi.org/...), others as plain numbers or missing entirely. All DOIs should be formatted as full URLs to enable direct access, following current standards.",
"author_name_formatting": "Author names are inconsistently formatted, with some entries using full names, others initials, and varying punctuation. Standardizing author name formats (e.g., Lastname, First Initials) across all references will enhance clarity.",
"publication_date_formatting": "Publication dates are variably formatted, with some including month and year, others only year. Consistent date formatting (e.g., Year, Month) across all references will improve chronological clarity.",
"journal_name_formatting": "Journal names are inconsistently italicized or abbreviated; some are fully spelled out, others abbreviated. Consistent formatting\u2014either full journal names italicized or standardized abbreviations\u2014will improve uniformity.",
"volume_issue_page_formatting": "Volume, issue, and page numbers are inconsistently formatted; some entries include parentheses, others omit issue numbers or page ranges. Uniform formatting following a style guide (e.g., Volume(Issue), pages) is recommended.",
"cross_reference_accuracy": "In-text citations do not always match reference list entries, with some missing references or mismatched numbering. Cross-checking all citations to ensure they correctly correspond to the reference list is crucial for scholarly integrity."
},
"summary": "Overall, the manuscript demonstrates a significant inconsistency in citation formatting and style. Addressing these issues by standardizing in-text citation formats, completing reference details, uniformly formatting journal titles, DOIs, author names, and publication dates, and ensuring accurate cross-referencing will markedly improve the scholarly quality, professionalism, and readability of the document. Implementing a consistent citation style aligned with journal guidelines is strongly recommended."
}

193
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@@ -1,193 +0,0 @@
{
"audience_alignment_score": 8,
"critical_remarks": [
{
"category": "methodology",
"location": "Section 2.2",
"issue": "While the methodology is detailed, some technical descriptions, such as hyperparameter tuning and feature preprocessing, assume prior knowledge and lack explicit explanations for non-expert readers.",
"severity": "medium",
"impact": "Could hinder understanding for readers unfamiliar with machine learning procedures, limiting accessibility to a broader academic audience."
},
{
"category": "visuals",
"location": "Throughout the Results section",
"issue": "The figures and tables are referenced with minimal descriptive context, and the visual complexity may overwhelm readers without accompanying interpretative summaries.",
"severity": "medium",
"impact": "May reduce clarity of key findings, affecting audience comprehension and engagement."
},
{
"category": "references",
"location": "Bibliography",
"issue": "While extensive, the references predominantly cite technical and methodological papers; there is limited integration of recent clinical or behavioral science literature that contextualizes the findings.",
"severity": "low",
"impact": "Slightly limits the interdisciplinary appeal and depth of contextual understanding for clinicians or behavioral scientists."
},
{
"category": "results",
"location": "Section 3.2",
"issue": "Results are densely presented with numerous metrics and statistical details, which may obscure the overarching narrative of model performance.",
"severity": "high",
"impact": "Could challenge readers' ability to grasp the main implications, reducing overall engagement."
},
{
"category": "discussion",
"location": "Section 4",
"issue": "While the discussion covers technical performance, it lacks sufficient exploration of practical implications, limitations, or potential real-world applications beyond technical metrics.",
"severity": "medium",
"impact": "May limit the relevance for practitioners seeking actionable insights, affecting broader applicability."
},
{
"category": "conclusion",
"location": "Section 4.4",
"issue": "The conclusion summarizes findings but does not clearly articulate future directions or specific recommendations for implementation.",
"severity": "low",
"impact": "Reduces the perceived utility of the research for advancing practice or policy."
},
{
"category": "terminology",
"location": "Throughout the document",
"issue": "Use of technical terms like 'AUC', 'F1-score', 'stratified 10-fold cross-validation' assumes familiarity; some terms could benefit from brief definitions or explanations.",
"severity": "medium",
"impact": "May alienate or confuse readers outside the machine learning or digital health research communities."
},
{
"category": "writing style",
"location": "Abstract and sections",
"issue": "The writing is formal and technical but occasionally verbose, which could hinder readability for a broader academic audience.",
"severity": "low",
"impact": "Potentially reduces accessibility and engagement for interdisciplinary readers."
},
{
"category": "section organization",
"location": "Entire document",
"issue": "While sections are logically ordered, some subsections (e.g., detailed tables and appendices) could be better integrated with main text to improve flow and contextual understanding.",
"severity": "medium",
"impact": "May cause readers to overlook key insights or struggle to connect detailed data with overarching themes."
},
{
"category": "visual elements",
"location": "Figures 1-8 and tables",
"issue": "Visuals are data-rich but lack sufficient interpretative captions or summaries that highlight key takeaways.",
"severity": "medium",
"impact": "Could diminish the clarity and immediate understanding of complex data, affecting engagement."
},
{
"category": "references",
"location": "Bibliography",
"issue": "The referencing style is consistent but predominantly includes journal articles; inclusion of more gray literature or recent reviews could enhance comprehensiveness.",
"severity": "low",
"impact": "Minimal impact but could improve depth for readers seeking broader context."
}
],
"improvement_suggestions": [
{
"original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.",
"improved_version": "Given the extensive behavioral data collected by mHealth interventions, it is pertinent to investigate the extent to which nonadherence can be accurately predicted using these data.",
"explanation": "Clarifies the research question with more precise language, enhancing clarity and focus for the audience.",
"location": "Abstract",
"category": "organization",
"focus": "clarity"
},
{
"original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.",
"improved_version": "In Vivira, our models successfully identified approximately 94% of nonadherent users\u2014those completing fewer than eight exercises weekly\u2014between Weeks 2 and 13, with an average AUC of 0.95.",
"explanation": "Rephrases for clarity, explicitly linking the metric to the adherence definition, aiding comprehension.",
"location": "Abstract",
"category": "results",
"focus": "clarity"
},
{
"original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.",
"improved_version": "Due to regulatory restrictions, only users who explicitly consented under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included in the analysis.",
"explanation": "Simplifies and clarifies regulatory context, making it more accessible to readers unfamiliar with legal details.",
"location": "Section 2.1.1",
"category": "terminology",
"focus": "clarity"
},
{
"original_text": "Figures and tables are referenced with minimal descriptive context, which may overwhelm readers without accompanying interpretative summaries.",
"improved_version": "Each figure and table should be accompanied by a concise interpretative caption that highlights key findings, such as trends or significant differences, to guide reader understanding.",
"explanation": "Enhances visual comprehension by providing context, improving engagement and clarity.",
"location": "Throughout Results",
"category": "visuals",
"focus": "organization"
},
{
"original_text": "Results are densely presented with numerous metrics and statistical details, which may obscure the overarching narrative of model performance.",
"improved_version": "Summarize key performance metrics in a narrative that emphasizes overall trends and implications, reserving detailed statistics for supplementary tables or appendices.",
"explanation": "Improves readability by focusing on main messages, making complex data more digestible.",
"location": "Section 3.2",
"category": "results",
"focus": "organization"
},
{
"original_text": "While the discussion covers technical performance, it lacks sufficient exploration of practical implications, limitations, or potential real-world applications beyond technical metrics.",
"improved_version": "Expand the discussion to include practical implications, potential implementation strategies, and limitations, thereby bridging the gap between technical performance and real-world applicability.",
"explanation": "Enhances relevance for practitioners and policymakers, increasing audience engagement.",
"location": "Section 4",
"category": "discussion",
"focus": "application"
},
{
"original_text": "The conclusion summarizes findings but does not clearly articulate future directions or specific recommendations for implementation.",
"improved_version": "Conclude with specific recommendations for future research, potential clinical integration, and policy considerations to maximize the impact of these predictive models.",
"explanation": "Provides actionable guidance, increasing the utility of the research for stakeholders.",
"location": "Section 4.4",
"category": "conclusion",
"focus": "organization"
},
{
"original_text": "Use of technical terms like 'AUC', 'F1-score', 'stratified 10-fold cross-validation' assumes prior knowledge; some terms could benefit from brief definitions or explanations.",
"improved_version": "Include brief explanations of key technical terms (e.g., 'AUC' as the area under the receiver operating characteristic curve) when first introduced to enhance accessibility.",
"explanation": "Makes the content more approachable for interdisciplinary audiences, broadening readership.",
"location": "Throughout technical sections",
"category": "terminology",
"focus": "clarity"
},
{
"original_text": "The writing is formal and technical but occasionally verbose, which could hinder readability for a broader academic audience.",
"improved_version": "Simplify complex sentences and reduce jargon where possible, aiming for concise, clear language that maintains technical accuracy.",
"explanation": "Improves readability and engagement across diverse academic backgrounds.",
"location": "Abstract and main sections",
"category": "writing style",
"focus": "clarity"
},
{
"original_text": "While sections are logically ordered, some subsections (e.g., detailed tables and appendices) could be better integrated with main text to improve flow and contextual understanding.",
"improved_version": "Integrate key findings from tables and appendices into the main narrative with explicit references, ensuring smooth flow and contextual clarity.",
"explanation": "Enhances coherence and helps readers connect detailed data with overarching themes.",
"location": "Section organization",
"category": "organization",
"focus": "organization"
},
{
"original_text": "Visuals are data-rich but lack sufficient interpretative captions or summaries that highlight key takeaways.",
"improved_version": "Add descriptive captions and brief summaries for each figure and table that explicitly state the main insights and their relevance to the research questions.",
"explanation": "Facilitates quicker understanding and emphasizes the significance of visual data.",
"location": "Figures 1-8 and tables",
"category": "visuals",
"focus": "organization"
},
{
"original_text": "The referencing style is consistent but predominantly includes journal articles; inclusion of more gray literature or recent reviews could enhance comprehensiveness.",
"improved_version": "Incorporate recent review articles, gray literature, and policy documents where relevant to provide a more comprehensive and current literature context.",
"explanation": "Broadens the scope and relevance, appealing to a wider academic and practitioner audience.",
"location": "Bibliography",
"category": "references",
"focus": "depth"
}
],
"detailed_feedback": {
"technical_depth": "The manuscript demonstrates a high level of technical depth, appropriate for researchers familiar with machine learning and digital health. However, some methodological details, such as hyperparameter tuning and feature preprocessing, could be elaborated to enhance transparency and reproducibility for advanced readers.",
"terminology_usage": "The use of field-specific terms like 'AUC', 'F1-score', and 'stratified 10-fold cross-validation' aligns with standard scientific conventions. Nonetheless, providing brief definitions or context for these terms upon first mention would improve accessibility for interdisciplinary audiences or newcomers to ML methods.",
"writing_formality": "The writing maintains a formal, academic tone suitable for scholarly publication. To improve readability for a broader audience, consider reducing verbosity and simplifying complex sentences without sacrificing technical accuracy.",
"section_organization": "The overall structure follows a logical progression, with clear separation of Introduction, Methods, Results, and Discussion. However, integrating key findings from detailed tables into the main narrative and ensuring smooth transitions between sections would enhance flow and comprehension.",
"visual_integration": "Figures and tables are comprehensive but could benefit from more interpretative captions that highlight main insights. Embedding summaries or key takeaways alongside visuals would aid reader understanding and engagement.",
"reference_style": "References are consistently formatted and relevant, but expanding to include recent reviews and gray literature would provide a more comprehensive background and contextual foundation.",
"methodology_detail": "The methodology is described with sufficient technical detail for replication but could include more explanation of specific ML procedures, such as hyperparameter selection rationale and feature engineering steps, to improve transparency.",
"results_presentation": "Results are detailed with numerous metrics, which may overwhelm readers. Summarizing key performance trends and implications in the main text, with detailed statistics in appendices, would improve clarity.",
"discussion_depth": "The discussion addresses technical performance well but could be expanded to explore practical implications, limitations, and potential real-world applications, making the findings more relevant for practitioners.",
"conclusion_format": "The conclusion summarizes main findings but would benefit from explicit future directions, recommendations for implementation, and broader impact statements to enhance utility and engagement."
},
"summary": "Overall, the manuscript exhibits strong technical rigor and is well-suited for an academic audience familiar with digital health and machine learning. To broaden its accessibility and impact, targeted improvements in clarity, visual integration, and contextual explanations are recommended. These enhancements would facilitate better engagement across interdisciplinary audiences, including clinicians, behavioral scientists, and policymakers, thereby maximizing the research's translational potential."
}

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@@ -1,127 +0,0 @@
{
"visual_presentation_score": 2,
"critical_remarks": [
{
"category": "Figures",
"location": "Figures 1, 3, 5, 6, 8",
"issue": "All figures are missing visual content; only placeholder captions and descriptions are provided, with no actual images or diagrams included.",
"severity": "high",
"impact": "Severely limits the reader's ability to interpret data visually, reducing comprehension and engagement. Visuals are crucial for understanding trends, patterns, and results."
},
{
"category": "Tables",
"location": "Tables 5-11",
"issue": "Tables are densely packed with numerical data but lack clear formatting cues such as alternating row colors, bold headers, or spacing to enhance readability.",
"severity": "medium",
"impact": "Hinders quick scanning and comparison of data, potentially leading to misinterpretation or oversight of key statistics."
},
{
"category": "Visual element placement",
"location": "Throughout the manuscript",
"issue": "Figures and tables are embedded within dense text blocks without strategic placement or referencing, making navigation cumbersome.",
"severity": "medium",
"impact": "Reduces flow and readability, forcing readers to search for visual aids and disrupting comprehension."
},
{
"category": "Caption completeness",
"location": "All figures and tables",
"issue": "Captions are lengthy but often lack concise summaries of key insights or explanations of axes, units, or significance.",
"severity": "low",
"impact": "Limits the utility of visuals for quick understanding; captions should clarify what the visual demonstrates."
},
{
"category": "Color scheme",
"location": "Figures 6-8",
"issue": "Color schemes are not specified, and given the absence of actual visuals, it's unclear if contrast and accessibility considerations (e.g., for color-blind readers) are addressed.",
"severity": "low",
"impact": "Potential accessibility issues if color is used without contrast considerations; proper color choices enhance clarity."
},
{
"category": "Data visualization effectiveness",
"location": "Figures 6-8",
"issue": "Figures intended to show performance metrics are placeholders with no actual plots or charts, rendering them ineffective.",
"severity": "high",
"impact": "Prevents visual comparison of model performance over time, which is critical for understanding trends and results."
},
{
"category": "Visual hierarchy",
"location": "Throughout the manuscript",
"issue": "Lack of visual hierarchy; no use of font size, bolding, or spacing to differentiate headings, subheadings, and key data points.",
"severity": "medium",
"impact": "Makes navigation and comprehension more difficult, especially when scanning for key results."
},
{
"category": "Accessibility considerations",
"location": "Figures and tables",
"issue": "No evidence of accessibility features such as alt text, high contrast, or font size considerations.",
"severity": "low",
"impact": "Limits accessibility for readers with visual impairments or color vision deficiencies."
},
{
"category": "Consistency in visual style",
"location": "Throughout the manuscript",
"issue": "No consistent style for tables and figure captions; formatting varies, and visual elements are absent.",
"severity": "low",
"impact": "Reduces professional appearance and coherence of the manuscript."
},
{
"category": "Integration with text",
"location": "Throughout the manuscript",
"issue": "Visual elements are missing or poorly integrated; references to figures/tables are frequent but visuals are absent, making it hard to connect data with narrative.",
"severity": "high",
"impact": "Hinders understanding of key points and weakens the support visuals could provide."
}
],
"improvement_suggestions": [
{
"original_text": "Figure 1: Percentages of daily active users and cumulative percentage of users\u2019 last login across 90-Day program duration in Vivira (n = 8,372).",
"improved_version": "Figure 1: Line chart illustrating daily active user retention and cumulative last login percentage over the 90-day Vivira program. Include clear axes labels, legend, and high-resolution image.",
"explanation": "Visuals should clearly depict trends with labels and legends for quick interpretation, enhancing comprehension.",
"location": "Figures 1, 3, 5, 6, 8",
"category": "Figures",
"focus": "Clarity and effectiveness"
},
{
"original_text": "Tables 5-11: Descriptive statistics of users\u2019 number of active days and completed exercises per week in Vivira and Manoa.",
"improved_version": "Tables 5-11: Reformat tables with alternating row shading, bold headers, and consistent decimal places. Add summary notes highlighting key statistics at the top of each table.",
"explanation": "Improved formatting enhances readability, allows quick comparison, and emphasizes important data points.",
"location": "Tables 5-11",
"category": "Tables",
"focus": "Readability and clarity"
},
{
"original_text": "Throughout the manuscript",
"improved_version": "Embed visual elements close to relevant text sections, with clear references and consistent style. Use subheadings and spacing to delineate sections clearly.",
"explanation": "Strategic placement and consistent style improve flow and help readers connect visuals with narrative.",
"location": "Entire manuscript",
"category": "Visual placement",
"focus": "Flow and integration"
},
{
"original_text": "Captions are lengthy but lack concise summaries.",
"improved_version": "Revise captions to be concise yet informative, e.g., 'Figure 1: Retention and last login trends over 90 days in Vivira.' Include key insights or data points if space allows.",
"explanation": "Clear, concise captions facilitate quick understanding and contextualize visuals effectively.",
"location": "Figures and tables",
"category": "Caption completeness",
"focus": "Clarity and utility"
},
{
"original_text": "Color schemes are unspecified.",
"improved_version": "Apply high-contrast color schemes suitable for color-blind readers, such as color palettes from ColorBrewer. Use distinct colors for different data series and ensure all are distinguishable in grayscale.",
"explanation": "Enhances accessibility and ensures visuals are interpretable by all readers."
}
],
"detailed_feedback": {
"figure_quality": "Figures are absent; placeholder captions do not provide visual content. When included, should be high-resolution, clear, and well-labeled.",
"table_formatting": "Tables are dense with data, lacking visual cues for readability. Formatting improvements needed for clarity.",
"visual_placement": "Visuals are poorly integrated, often missing or placed without clear reference, disrupting flow.",
"caption_completeness": "Captions are lengthy but lack concise summaries and clarity about axes, units, and significance.",
"color_scheme": "Not specified; should use accessible, high-contrast palettes for clarity and accessibility.",
"data_visualization": "Effective data visualization is missing; inclusion of well-designed charts would significantly improve comprehension.",
"visual_hierarchy": "Lacking; visual elements do not differentiate headings, subheadings, or key data points, reducing clarity.",
"accessibility": "No explicit accessibility features; improvements needed for color contrast, font size, and descriptive alt text.",
"visual_consistency": "Inconsistent style and formatting across visuals; standardization would improve professionalism.",
"text_integration": "Visuals are not well integrated with the text; better referencing and placement are necessary for coherence."
},
"summary": "Overall, the manuscript's visual presentation is severely lacking, with missing figures, poorly formatted tables, and inadequate integration of visuals with the narrative. To enhance clarity, readability, and impact, the authors should incorporate high-quality, well-designed visual elements, improve formatting and placement, and ensure visuals support and complement the textual content effectively."
}

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[]

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{}

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true

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[]

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"Error in analysis: Error in analysis: 'NarrativeStructureAgent' object has no attribute 'analyze_organization'"

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0

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"Analysis failed due to error: Error in analysis: 'NarrativeStructureAgent' object has no attribute 'analyze_organization'"

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{
"originality_review": {
"originality_contribution_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"novelty_assessment": "",
"contribution_analysis": "",
"verification_status": "",
"comparative_analysis": "",
"advancement_evaluation": ""
},
"summary": "Error in analysis: Error analyzing originality and contribution: Error calling language model: Error code: 401 - {'error': {'message': 'Incorrect API key provided: your-api*****here. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}",
"error": true
},
"impact_review": {
"impact_significance_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"field_influence": "",
"broader_implications": "",
"future_research_impact": "",
"practical_applications": "",
"policy_implications": ""
},
"summary": "Error in analysis: Error analyzing impact and significance: Error calling language model: Error code: 401 - {'error': {'message': 'Incorrect API key provided: your-api*****here. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}",
"error": true
},
"language_review": {
"language_style_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"grammar_correctness": "",
"spelling_accuracy": "",
"punctuation_usage": "",
"sentence_structure": "",
"verb_tense_consistency": "",
"subject_verb_agreement": "",
"article_usage": "",
"preposition_usage": "",
"conjunction_usage": "",
"academic_conventions": ""
},
"summary": "Error in analysis: Error analyzing language style: Error calling language model: Error code: 401 - {'error': {'message': 'Incorrect API key provided: your-api*****here. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}",
"error": true
}
}

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import os
import json
import glob
from src.utils.pdf_parser import PDFParser
from src.reviewer_agents.controller_agent import ControllerAgent
from src.core.config import DEFAULT_MODEL
def process_pdf(pdf_path):
"""Process PDF and extract text, figures, and tables."""
parser = PDFParser(pdf_path)
# Extract all components
text = parser.extract_text()
metadata = parser.get_metadata()
images = parser.extract_images()
tables = parser.extract_tables()
return {
'text': text,
'metadata': metadata,
'images': images,
'tables': tables
}
def find_pdf_in_directory(directory):
"""Find the first PDF file in the specified directory."""
pdf_files = glob.glob(os.path.join(directory, "*.pdf"))
if not pdf_files:
raise FileNotFoundError(f"No PDF files found in {directory}")
return pdf_files[0] # Return the first PDF file found
def main():
# Find PDF in manuscripts directory
manuscripts_dir = "manuscripts"
try:
manuscript_path = find_pdf_in_directory(manuscripts_dir)
print(f"Found PDF: {os.path.basename(manuscript_path)}")
except FileNotFoundError as e:
print(f"Error: {e}")
return
# Process the manuscript
manuscript_data = process_pdf(manuscript_path)
# Initialize controller agent
controller = ControllerAgent(model=DEFAULT_MODEL)
# Run the analysis
results = controller.run_analysis(text=manuscript_data['text'])
# Save results
output_dir = "results"
os.makedirs(output_dir, exist_ok=True)
# Save manuscript data for reference
manuscript_data_file = os.path.join(output_dir, "manuscript_data.json")
with open(manuscript_data_file, "w") as f:
# Convert image data to base64 for JSON serialization
manuscript_json = manuscript_data.copy()
for img in manuscript_json['images']:
img['image_data'] = None # Remove binary image data for JSON
json.dump(manuscript_json, f, indent=2)
# Save individual agent results
for agent_name, result in results.items():
output_file = os.path.join(output_dir, f"{agent_name}_results.json")
with open(output_file, "w") as f:
json.dump(result, f, indent=2)
# Save combined results
combined_output = os.path.join(output_dir, "combined_results.json")
with open(combined_output, "w") as f:
json.dump(results, f, indent=2)
print(f"Analysis complete. Results saved to {output_dir}/")
if __name__ == "__main__":
main()

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#!/bin/bash
# Script to generate a report from the combined_results.json file
# Get the directory of this script
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
# Get the project root directory (parent of scripts directory)
PROJECT_ROOT="$( cd "$SCRIPT_DIR/.." && pwd )"
# Set paths
INPUT_FILE="$PROJECT_ROOT/results/combined_results.json"
OUTPUT_FILE="$PROJECT_ROOT/results/manuscript_report.md"
CONVERTER_SCRIPT="$PROJECT_ROOT/src/utils/json_to_report.py"
# Check if the input file exists
if [ ! -f "$INPUT_FILE" ]; then
echo "Error: Input file not found at $INPUT_FILE"
exit 1
fi
# Run the converter script
echo "Generating report from $INPUT_FILE..."
python "$CONVERTER_SCRIPT" --input "$INPUT_FILE" --output "$OUTPUT_FILE"
# Check if the report was generated successfully
if [ -f "$OUTPUT_FILE" ]; then
echo "Report generated successfully at $OUTPUT_FILE"
# Open the report if on macOS
if [[ "$OSTYPE" == "darwin"* ]]; then
open "$OUTPUT_FILE"
fi
else
echo "Error: Failed to generate report"
exit 1
fi

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from setuptools import setup, find_packages
setup(
name="manuscript_reviewer",
version="0.1",
packages=find_packages(),
install_requires=[
'openai>=1.0.0',
'python-dotenv>=0.19.0',
'PyPDF2>=3.0.0',
'langchain>=0.1.0',
'langchain-community>=0.0.10',
'typing-extensions>=4.0.0',
'requests>=2.31.0',
'python-json-logger>=2.0.0',
'nougat-ocr>=0.1.0',
'pdf2image>=1.16.3',
'pydantic>=2.0.0',
'pytest>=7.0.0',
'tqdm>=4.65.0',
'numpy>=1.24.0',
'pandas>=2.0.0'
],
)

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"""
This module contains the manuscript reviewer system.
"""

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from typing import Dict, Any, List
import json
import os
from datetime import datetime
from openai import OpenAI
from dotenv import load_dotenv
from .config import REPORT_TEMPLATE, DEFAULT_MODEL
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
class BaseReviewerAgent:
"""Base class for all reviewer agents."""
def __init__(self, model: str = "gpt-4.1-nano"):
"""
Initialize the base reviewer agent.
Args:
model (str): The language model to use
name (str): Name of the agent
category (str): Category of the agent (scientific_rigor)
"""
self.model = model
self.name = "Base_Reviewer_Agent"
self.category = "Scientific Rigor"
# Initialize OpenAI client with API key from environment
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError(f"OPENAI_API_KEY environment variable not set. Please check {env_path}")
# Print debug info
print(f"Using model: {model}")
print(f"API key found: {'Yes' if api_key else 'No'}")
self.client = OpenAI(api_key=api_key)
def llm(self, prompt: str) -> str:
"""Call OpenAI API with the given prompt."""
try:
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": "You are an expert academic reviewer. Provide detailed analysis in JSON format."},
{"role": "user", "content": prompt}
],
temperature=0.3,
response_format={"type": "json_object"}
)
return response.choices[0].message.content
except Exception as e:
raise Exception(f"Error calling language model: {str(e)}")
def create_report_template(self) -> Dict[str, Any]:
"""Create a new report template."""
template = REPORT_TEMPLATE.copy()
template["metadata"].update({
"agent_name": self.name,
"category": self.category,
"model_used": self.model,
"timestamp": datetime.now().isoformat()
})
return template
def analyze_section(self, text: str, section_name: str) -> Dict[str, Any]:
"""Analyze a specific section of the manuscript.
Args:
text (str): Text content to analyze
section_name (str): Name of the section being analyzed
Returns:
Dict[str, Any]: Analysis results
"""
prompt = f"""As a {self.name}, analyze the following {section_name} section:
{text}
Provide your analysis in the following JSON format:
{{
"score": <1-10>,
"remarks": [
"List of specific issues, questions, or observations"
],
"concrete_suggestions": [
"List of actionable steps for improvement"
],
"automated_improvements": [
"List of AI-generated improvements"
]
}}
Ensure your response is valid JSON and includes all required fields."""
try:
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": f"You are a {self.name} reviewer."},
{"role": "user", "content": prompt}
],
temperature=0.7
)
# Extract JSON from response
content = response.choices[0].message.content
start_idx = content.find('{')
end_idx = content.rfind('}') + 1
if start_idx >= 0 and end_idx > start_idx:
analysis = json.loads(content[start_idx:end_idx])
else:
raise ValueError("No JSON found in response")
# Create report with analysis
report = self.create_report_template()
report.update(analysis)
return report
except Exception as e:
print(f"Error analyzing section: {e}")
return self.create_report_template()
def save_report(self, report: Dict[str, Any], output_path: str) -> None:
"""Save the report to a file.
Args:
report (Dict[str, Any]): Report to save
output_path (str): Path to save the report
"""
try:
with open(output_path, 'w') as f:
json.dump(report, f, indent=2)
except Exception as e:
print(f"Error saving report: {e}")
def load_report(self, input_path: str) -> Dict[str, Any]:
"""Load a report from a file.
Args:
input_path (str): Path to load the report from
Returns:
Dict[str, Any]: Loaded report
"""
try:
with open(input_path, 'r') as f:
return json.load(f)
except Exception as e:
print(f"Error loading report: {e}")
return self.create_report_template()

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from typing import Dict, Any
import os
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# OpenAI API Configuration
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
raise ValueError("OPENAI_API_KEY environment variable is not set")
DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "gpt-4.1-nano")
# Agent configurations
AGENT_CONFIGS = {
"scientific_rigor": [
{
"name": "R1_Methodology_Agent",
"category": "Scientific Rigor",
"expertise": ["Research Design", "Methodology", "Experimental Setup"],
"focus_areas": [
"Methodology robustness",
"Experimental design",
"Control conditions",
"Sample size justification",
"Data collection procedures"
]
},
{
"name": "R2_Impact_Significance_Agent",
"category": "Scientific Rigor",
"expertise": ["Research Impact", "Scientific Significance", "Field Contribution"],
"focus_areas": [
"Scientific impact",
"Field contribution",
"Practical implications",
"Future research directions"
]
},
{
"name": "R3_Ethics_Compliance_Agent",
"category": "Scientific Rigor",
"expertise": ["Research Ethics", "Compliance", "Data Protection"],
"focus_areas": [
"Ethical considerations",
"Conflict of interest",
"Data privacy",
"Informed consent",
"Research integrity"
]
},
{
"name": "R4_Data_Code_Availability_Agent",
"category": "Scientific Rigor",
"expertise": ["Data Management", "Code Availability", "Reproducibility"],
"focus_areas": [
"Data availability",
"Code sharing",
"Documentation",
"Reproducibility",
"Access restrictions"
]
},
{
"name": "R5_Statistical_Rigor_Agent",
"category": "Scientific Rigor",
"expertise": ["Statistical Analysis", "Data Validation", "Statistical Methods"],
"focus_areas": [
"Statistical methods",
"Data validation",
"Statistical significance",
"Error analysis",
"Statistical reporting"
]
},
{
"name": "R6_Technical_Accuracy_Agent",
"category": "Scientific Rigor",
"expertise": ["Technical Content", "Mathematical Rigor", "Algorithm Analysis"],
"focus_areas": [
"Technical accuracy",
"Mathematical correctness",
"Algorithm validation",
"Technical clarity",
"Implementation details"
]
},
{
"name": "R7_Consistency_Agent",
"category": "Scientific Rigor",
"expertise": ["Logical Coherence", "Cross-section Analysis", "Consistency Checking"],
"focus_areas": [
"Logical coherence",
"Cross-section consistency",
"Terminology consistency",
"Results alignment",
"Conclusion support"
]
}
],
"writing_presentation": [
{
"name": "W1_Language_Style_Agent",
"category": "Writing and Presentation",
"expertise": ["Grammar", "Spelling", "Punctuation"],
"focus_areas": [
"Grammar correctness",
"Spelling accuracy",
"Punctuation usage",
"Sentence structure",
"Academic writing conventions"
]
},
{
"name": "W2_Narrative_Structure_Agent",
"category": "Writing and Presentation",
"expertise": ["Narrative Flow", "Structural Organization", "Logical Progression"],
"focus_areas": [
"Narrative coherence",
"Logical progression",
"Section transitions",
"Paragraph organization",
"Reader engagement"
]
},
{
"name": "W3_Clarity_Conciseness_Agent",
"category": "Writing and Presentation",
"expertise": ["Language Simplicity", "Jargon Reduction", "Conciseness"],
"focus_areas": [
"Language simplicity",
"Jargon usage",
"Wordiness",
"Readability",
"Information density"
]
},
{
"name": "W4_Terminology_Consistency_Agent",
"category": "Writing and Presentation",
"expertise": ["Terminology Consistency", "Notation Standards", "Acronym Usage"],
"focus_areas": [
"Term usage consistency",
"Notation consistency",
"Acronym usage",
"Variable naming",
"Definition consistency"
]
}
]
}
# Review criteria
REVIEW_CRITERIA = {
"scientific_rigor": {
"methodology": {
"weight": 0.15,
"criteria": [
"Research design appropriateness",
"Methodology robustness",
"Experimental setup completeness",
"Control conditions adequacy",
"Sample size justification"
]
},
"impact": {
"weight": 0.15,
"criteria": [
"Scientific significance",
"Field contribution",
"Practical implications",
"Future research potential"
]
},
"ethics": {
"weight": 0.15,
"criteria": [
"Ethical considerations",
"Conflict of interest disclosure",
"Data privacy protection",
"Informed consent procedures",
"Research integrity"
]
},
"data_code": {
"weight": 0.15,
"criteria": [
"Data availability",
"Code sharing",
"Documentation completeness",
"Reproducibility",
"Access restrictions justification"
]
},
"statistics": {
"weight": 0.15,
"criteria": [
"Statistical methods appropriateness",
"Data validation",
"Statistical significance",
"Error analysis",
"Statistical reporting"
]
},
"technical": {
"weight": 0.15,
"criteria": [
"Technical accuracy",
"Mathematical correctness",
"Algorithm validation",
"Technical clarity",
"Implementation details"
]
},
"consistency": {
"weight": 0.10,
"criteria": [
"Logical coherence",
"Cross-section consistency",
"Terminology consistency",
"Results alignment",
"Conclusion support"
]
}
},
"writing_presentation": {
"language_style": {
"weight": 0.25,
"criteria": [
"Grammar correctness",
"Spelling accuracy",
"Punctuation usage",
"Sentence structure",
"Academic writing conventions"
]
},
"narrative_structure": {
"weight": 0.25,
"criteria": [
"Narrative coherence",
"Logical progression",
"Section transitions",
"Paragraph organization",
"Reader engagement"
]
},
"clarity_conciseness": {
"weight": 0.25,
"criteria": [
"Language simplicity",
"Jargon usage",
"Wordiness",
"Readability",
"Information density"
]
},
"terminology_consistency": {
"weight": 0.25,
"criteria": [
"Term usage consistency",
"Notation consistency",
"Acronym usage",
"Variable naming",
"Definition consistency"
]
}
}
}
# Report template
REPORT_TEMPLATE = {
"metadata": {
"timestamp": "",
"agent_name": "",
"category": "",
"model_used": "",
"version": "1.0"
},
"analysis": {
"overall_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {},
"summary": ""
}
}
# Controller Agent Configuration
CONTROLLER_CONFIG = {
"review_steps": [
"initial_analysis",
"agent_review_comparison",
"remark_ranking",
"report_generation"
],
"output_formats": ["json", "text"],
"score_range": (1, 10)
}
# PDF Processing Configuration
PDF_PROCESSOR_CONFIG = {
"supported_formats": ["pdf"],
"max_pages": 100,
"image_quality": "high",
"ocr_enabled": True
}
# File Paths
PATHS = {
"manuscripts": "manuscripts/",
"results": "results/",
"tests": "tests/",
"logs": "logs/"
}
# Create directories if they don't exist
for path in PATHS.values():
os.makedirs(path, exist_ok=True)

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@@ -1,124 +0,0 @@
from typing import Dict, Any, List
import json
from datetime import datetime
class ReportTemplate:
"""Template for generating standardized review reports."""
def __init__(self):
"""Initialize the report template."""
self.template = {
"metadata": {
"timestamp": datetime.now().isoformat(),
"agent_name": "",
"category": "",
"model_used": "",
"version": "1.0"
},
"analysis": {
"overall_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {},
"summary": ""
}
}
def generate_report(self, agent_name: str, category: str, model: str,
analysis_results: Dict[str, Any], output_path: str) -> None:
"""
Generate a standardized review report.
Args:
agent_name (str): Name of the agent
category (str): Category of the agent
model (str): Model used for analysis
analysis_results (Dict[str, Any]): Results of the analysis
output_path (str): Path to save the report
"""
report = self.template.copy()
report["metadata"].update({
"agent_name": agent_name,
"category": category,
"model_used": model
})
report["analysis"].update(analysis_results)
with open(output_path, 'w') as f:
f.write(f"Review Report - {agent_name}\n")
f.write("=" * (len(agent_name) + 13) + "\n\n")
f.write("Metadata\n")
f.write("-" * 8 + "\n")
f.write(json.dumps(report["metadata"], indent=2))
f.write("\n\n")
f.write("Analysis Results\n")
f.write("-" * 16 + "\n")
f.write(json.dumps(report["analysis"], indent=2))
f.write("\n")
@staticmethod
def create_template(agent_name: str, section_name: str) -> Dict[str, Any]:
"""Create a standardized report template.
Args:
agent_name (str): Name of the reviewer agent
section_name (str): Name of the section being reviewed
Returns:
Dict[str, Any]: Standardized report template
"""
return {
"agent_name": agent_name,
"section_name": section_name,
"timestamp": datetime.now().isoformat(),
"score": None, # 1-10 score
"remarks": [], # List of issues, questions, or observations
"concrete_suggestions": [], # Actionable steps for each remark
"automated_suggestions": [], # AI-generated improvements
"section_specific_analysis": {} # Additional section-specific analysis
}
@staticmethod
def save_report(report: Dict[str, Any], output_dir: str) -> str:
"""Save the report to a text file.
Args:
report (Dict[str, Any]): The report to save
output_dir (str): Directory to save the report in
Returns:
str: Path to the saved report file
"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"{report['agent_name'].lower().replace(' ', '_')}_{timestamp}.txt"
filepath = f"{output_dir}/{filename}"
with open(filepath, 'w') as f:
f.write(f"Review Report - {report['agent_name']}\n")
f.write(f"Section: {report['section_name']}\n")
f.write(f"Timestamp: {report['timestamp']}\n\n")
f.write(f"Score: {report['score']}/10\n\n")
f.write("Remarks:\n")
for remark in report['remarks']:
f.write(f"- {remark}\n")
f.write("\n")
f.write("Concrete Suggestions:\n")
for suggestion in report['concrete_suggestions']:
f.write(f"- {suggestion}\n")
f.write("\n")
f.write("Automated Suggestions:\n")
for suggestion in report['automated_suggestions']:
f.write(f"- {suggestion}\n")
f.write("\n")
if report['section_specific_analysis']:
f.write("Section-Specific Analysis:\n")
f.write(json.dumps(report['section_specific_analysis'], indent=2))
return filepath

3
Backup/V5_multi_agent2/src/reviewer_agents/__init__.py
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@@ -1,3 +0,0 @@
"""
This package contains the reviewer agents for manuscript analysis.
"""

148
Backup/V5_multi_agent2/src/reviewer_agents/controller_agent.py
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@@ -1,148 +0,0 @@
from typing import Dict, Any, List
import json
import os
from datetime import datetime
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from ..core.base_agent import BaseReviewerAgent
from ..core.report_template import ReportTemplate
# Section agents
from .section.S1_title_keywords_agent import TitleKeywordsAgentS1
from .section.S2_abstract_agent import AbstractAgentS2
from .section.S3_introduction_agent import IntroductionAgentS3
from .section.S4_literature_review_agent import LiteratureReviewAgentS4
from .section.S5_methodology_agent import MethodologyAgentS5
from .section.S6_results_agent import ResultsAgentS6
from .section.S7_discussion_agent import DiscussionAgentS7
from .section.S8_conclusion_agent import ConclusionAgentS8
from .section.S9_references_agent import ReferencesAgentS9
from .section.S10_supplementary_materials_agent import SupplementaryMaterialsAgentS10
# Rigor agents
from .rigor.R1_originality_contribution_agent import OriginalityContributionAgent
from .rigor.R2_impact_significance_agent import ImpactSignificanceAgent
from .rigor.R3_ethics_compliance_agent import EthicsComplianceAgent
from .rigor.R4_data_code_availability_agent import DataCodeAvailabilityAgent
from .rigor.R5_statistical_rigor_agent import StatisticalRigorAgent
from .rigor.R6_technical_accuracy_agent import TechnicalAccuracyAgent
from .rigor.R7_consistency_agent import ConsistencyAgent
# Writing agents
from .writing.W1_language_style_agent import LanguageStyleAgent
from .writing.W2_narrative_structure_agent import NarrativeStructureAgent
from .writing.W3_clarity_conciseness_agent import ClarityConcisenessAgent
from .writing.W4_terminology_consistency_agent import TerminologyConsistencyAgent
from .writing.W5_inclusive_language_agent import InclusiveLanguageAgent
from .writing.W6_citation_formatting_agent import CitationFormattingAgent
from .writing.W7_target_audience_agent import TargetAudienceAlignmentAgent
from .writing.W8_visual_presentation_agent import VisualPresentationAgentW8
class ControllerAgent:
"""Controller agent that coordinates all reviewer agents."""
def __init__(self, model="gpt-4.1-nano"):
self.model = model
self.agents = {
# Section agents
'S1': TitleKeywordsAgentS1(model),
'S2': AbstractAgentS2(model),
'S3': IntroductionAgentS3(model),
'S4': LiteratureReviewAgentS4(model),
'S5': MethodologyAgentS5(model),
'S6': ResultsAgentS6(model),
'S7': DiscussionAgentS7(model),
'S8': ConclusionAgentS8(model),
'S9': ReferencesAgentS9(model),
'S10': SupplementaryMaterialsAgentS10(model),
# Rigor agents
'R1': OriginalityContributionAgent(model),
'R2': ImpactSignificanceAgent(model),
'R3': EthicsComplianceAgent(model),
'R4': DataCodeAvailabilityAgent(model),
'R5': StatisticalRigorAgent(model),
'R6': TechnicalAccuracyAgent(model),
'R7': ConsistencyAgent(model),
# Writing agents
'W1': LanguageStyleAgent(model),
'W2': NarrativeStructureAgent(model),
'W3': ClarityConcisenessAgent(model),
'W4': TerminologyConsistencyAgent(model),
'W5': InclusiveLanguageAgent(model),
'W6': CitationFormattingAgent(model),
'W7': TargetAudienceAlignmentAgent(model),
'W8': VisualPresentationAgentW8(model)
}
def run_analysis(self, text: str) -> Dict[str, Any]:
"""Runs analyses using all agents."""
try:
# Determine research type
research_type = self._determine_research_type(text)
# Run analyses for each agent
results = {}
# Run section agent analyses
results["S1"] = self.agents["S1"].analyze_title_keywords(text, research_type)
results["S2"] = self.agents["S2"].analyze_abstract(text, research_type)
results["S3"] = self.agents["S3"].analyze_introduction(text, research_type)
results["S4"] = self.agents["S4"].analyze_literature_review(text, research_type)
results["S5"] = self.agents["S5"].analyze_methodology(text, research_type)
results["S6"] = self.agents["S6"].analyze_results(text, research_type)
results["S7"] = self.agents["S7"].analyze_discussion(text, research_type)
results["S8"] = self.agents["S8"].analyze_conclusion(text, research_type)
results["S9"] = self.agents["S9"].analyze_references(text, research_type)
results["S10"] = self.agents["S10"].analyze_supplementary_materials(text, research_type)
# Run rigor agent analyses
results["R1"] = self.agents["R1"].analyze_originality_contribution(text, research_type)
results["R2"] = self.agents["R2"].analyze_impact_significance(text, research_type)
results["R3"] = self.agents["R3"].analyze_ethics_compliance(text, research_type)
results["R4"] = self.agents["R4"].analyze_data_code_availability(text, research_type)
results["R5"] = self.agents["R5"].analyze_statistical_rigor(text, research_type)
results["R6"] = self.agents["R6"].analyze_technical_accuracy(text, research_type)
results["R7"] = self.agents["R7"].analyze_consistency(text, research_type)
# Run writing agent analyses
results["W1"] = self.agents["W1"].analyze_language_style(text, research_type)
results["W2"] = self.agents["W2"].analyze_narrative_structure(text, research_type)
results["W3"] = self.agents["W3"].analyze_clarity_conciseness(text, research_type)
results["W4"] = self.agents["W4"].analyze_terminology_consistency(text, research_type)
results["W5"] = self.agents["W5"].analyze_inclusive_language(text, research_type)
results["W6"] = self.agents["W6"].analyze_citation_formatting(text, research_type)
results["W7"] = self.agents["W7"].analyze_target_audience_alignment(text, research_type)
results["W8"] = self.agents["W8"].analyze_visual_presentation(text, research_type)
return results
except Exception as e:
return self._generate_error_report(f"Error in analysis: {str(e)}")
def _determine_research_type(self, text: str) -> str:
"""Determine the type of research paper."""
# Simple heuristic based on keywords
text_lower = text.lower()
if any(word in text_lower for word in ['experiment', 'methodology', 'data collection']):
return 'experimental'
elif any(word in text_lower for word in ['review', 'literature', 'meta-analysis']):
return 'review'
elif any(word in text_lower for word in ['theory', 'framework', 'model']):
return 'theoretical'
else:
return 'general'
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"error": True,
"message": f"Error in analysis: {error_message}",
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {},
"summary": f"Analysis failed due to error: {error_message}"
}

343
Backup/V5_multi_agent2/src/reviewer_agents/report/R1_comprehensive_report_agent.py
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@@ -1,343 +0,0 @@
from typing import Dict, Any, List
import json
import os
from datetime import datetime
from ...core.base_agent import BaseReviewerAgent
from openai import OpenAI
from docx import Document
from docx.shared import Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
class ComprehensiveReportAgent(BaseReviewerAgent):
"""Agent responsible for generating a comprehensive report using GPT-4.1."""
def __init__(self, model="gpt-4.1"):
super().__init__(model)
self.name = "R1_Comprehensive_Report_Agent"
self.category = "Report Generation"
# Initialize OpenAI
self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
self.model = model
def generate_comprehensive_report(self, manuscript_text: str, markdown_report: str) -> Dict[str, Any]:
"""Generates a comprehensive report using GPT-4.1."""
try:
# Split the markdown report into sections
sections = self._split_markdown_into_sections(markdown_report)
# Process each section separately
analysis = {}
for section_name, section_content in sections.items():
# Generate prompt for this section
prompt = f"""You are a scientific manuscript reviewer. Your task is to analyze this section of the manuscript and markdown feedback, then provide a structured JSON response with detailed feedback.
Section: {section_name}
Manuscript Text:
{manuscript_text[:1000]}... # First 1000 chars for context
Section Feedback:
{section_content}
Structure your response as a JSON object with the following format:
{{
"{section_name}": {{
"score": 0-10, // Numerical score for this section
"summary": "A concise summary of the key points and recommendations for this section",
"remarks": [
{{
"remark": "What could be an issue?",
"original_text": "Citation of the text passage related to the issue",
"improved_version": "New improved text",
"explanation": "How does this new version improve the paper relative to the issue"
}}
],
"not_applicable": false // Set to true if section doesn't apply
}}
}}
Important:
1. Respond ONLY with the JSON object
2. Do not include any additional text or explanations
3. Ensure all text fields are properly escaped for JSON
4. Make sure the response is valid JSON that can be parsed
5. Only include genuinely helpful feedback
6. For sections that don't apply, set not_applicable to true
7. Focus on providing clear, actionable feedback without mentioning sources
8. Include numerical scores for each section
9. Extract specific feedback from the markdown report and incorporate it into your response
10. If a section has no feedback in the markdown report, provide your own assessment based on the manuscript
11. Ensure each section has at least one remark if applicable
12. Make sure the feedback is specific and actionable
13. Use the exact scores and feedback from the markdown report when available
14. Maintain the same level of detail and specificity as the markdown report
15. Include all critical remarks and improvement suggestions from the markdown report
"""
# Generate content with GPT-4.1
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": "You are a scientific manuscript reviewer. Your task is to analyze the manuscript and markdown feedback, then provide a structured JSON response with detailed feedback for each section."},
{"role": "user", "content": prompt}
],
temperature=0.1, # Lower temperature for more focused output
max_tokens=4000, # Increased token limit
top_p=0.8,
frequency_penalty=0.0,
presence_penalty=0.0
)
response_text = response.choices[0].message.content.strip()
# Try to parse the JSON response
try:
section_analysis = json.loads(response_text)
analysis.update(section_analysis)
except json.JSONDecodeError as e:
# If JSON parsing fails, try to extract JSON from the response
import re
json_match = re.search(r'\{.*\}', response_text, re.DOTALL)
if json_match:
try:
section_analysis = json.loads(json_match.group())
analysis.update(section_analysis)
except json.JSONDecodeError:
# If still fails, try to clean the response
cleaned_text = re.sub(r'[\x00-\x1f\x7f-\x9f]', '', json_match.group())
section_analysis = json.loads(cleaned_text)
analysis.update(section_analysis)
else:
print(f"Could not parse JSON for section {section_name}: {response_text}")
continue
# Validate the analysis structure
required_sections = {
"Section-Specific Agents": [f"S{i}" for i in range(1, 11)],
"Rigor Agents": [f"R{i}" for i in range(1, 8)],
"Writing Agents": [f"W{i}" for i in range(1, 9)]
}
for section_name, subsections in required_sections.items():
for subsection in subsections:
if subsection not in analysis:
analysis[subsection] = {
"score": 0,
"summary": "No feedback available for this section.",
"remarks": [],
"not_applicable": True
}
# Generate Word document report
doc_path = self._generate_word_report(analysis, manuscript_text, markdown_report)
return {
"report_generation_score": 10, # Assuming successful generation
"doc_path": doc_path,
"analysis": analysis,
"summary": "Successfully generated comprehensive report",
"error": False
}
except Exception as e:
return self._generate_error_report(f"Error generating comprehensive report: {str(e)}")
def _split_markdown_into_sections(self, markdown_report: str) -> Dict[str, str]:
"""Split the markdown report into sections."""
sections = {}
# Define section patterns
section_patterns = {
"Section-Specific Agents": {
"S1": "## S1 - Title and Keywords",
"S2": "## S2 - Abstract",
"S3": "## S3 - Introduction",
"S4": "## S4 - Literature Review",
"S5": "## S5 - Methodology",
"S6": "## S6 - Results",
"S7": "## S7 - Discussion",
"S8": "## S8 - Conclusion",
"S9": "## S9 - References",
"S10": "## S10 - Supplementary Materials"
},
"Rigor Agents": {
"R1": "## R1 - Originality and Contribution",
"R2": "## R2 - Impact and Significance",
"R3": "## R3 - Ethics and Compliance",
"R4": "## R4 - Data and Code Availability",
"R5": "## R5 - Statistical Rigor",
"R6": "## R6 - Technical Accuracy",
"R7": "## R7 - Consistency"
},
"Writing Agents": {
"W1": "## W1 - Language and Style",
"W2": "## W2 - Narrative and Structure",
"W3": "## W3 - Clarity and Conciseness",
"W4": "## W4 - Terminology Consistency",
"W5": "## W5 - Inclusive Language",
"W6": "## W6 - Citation Formatting",
"W7": "## W7 - Target Audience Alignment",
"W8": "## W8 - Visual Presentation"
}
}
# Split the report into sections
lines = markdown_report.split('\n')
current_section = None
current_content = []
for line in lines:
# Check if this line starts a new section
found_section = False
for category, patterns in section_patterns.items():
for section_id, pattern in patterns.items():
if line.startswith(pattern):
# Save the previous section if it exists
if current_section:
sections[current_section] = '\n'.join(current_content)
# Start a new section
current_section = section_id
current_content = [line]
found_section = True
break
if found_section:
break
# If not a new section, add to current content
if not found_section and current_section:
current_content.append(line)
# Save the last section
if current_section:
sections[current_section] = '\n'.join(current_content)
return sections
def _generate_word_report(self, analysis: Dict[str, Any], manuscript_text: str, markdown_report: str) -> str:
"""Generates a Word document report."""
# Create output directory if it doesn't exist
output_dir = "results/reports"
os.makedirs(output_dir, exist_ok=True)
# Generate filename with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
doc_path = os.path.join(output_dir, f"comprehensive_report_{timestamp}.docx")
# Create Word document
doc = Document()
# Add title
title = doc.add_heading("Comprehensive Manuscript Review Report", 0)
title.alignment = WD_ALIGN_PARAGRAPH.CENTER
# Add timestamp
timestamp_para = doc.add_paragraph()
timestamp_para.add_run(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
doc.add_paragraph()
# Add overall summary
doc.add_heading("Overall Assessment", 1)
summary_para = doc.add_paragraph()
summary_para.add_run("This report provides a comprehensive review of the manuscript, analyzing its content, structure, and scientific rigor. The assessment is organized into three main categories: Section-Specific Analysis, Rigor Assessment, and Writing Quality Evaluation.")
doc.add_paragraph()
# Process each section
sections = {
"Section-Specific Analysis": {f"S{i}": analysis.get(f"S{i}", {}) for i in range(1, 11)},
"Rigor Assessment": {f"R{i}": analysis.get(f"R{i}", {}) for i in range(1, 8)},
"Writing Quality Evaluation": {f"W{i}": analysis.get(f"W{i}", {}) for i in range(1, 9)}
}
for section_name, subsections in sections.items():
# Add section heading
doc.add_heading(section_name, 1)
# Add section introduction
intro_text = {
"Section-Specific Analysis": "This section provides a detailed analysis of each component of the manuscript, from title to supplementary materials.",
"Rigor Assessment": "This section evaluates the scientific rigor, methodology, and technical aspects of the research.",
"Writing Quality Evaluation": "This section assesses the manuscript's writing quality, clarity, and adherence to academic standards."
}
doc.add_paragraph(intro_text[section_name])
doc.add_paragraph()
for subsection_name, subsection_data in subsections.items():
if subsection_data.get("not_applicable", False):
para = doc.add_paragraph()
para.add_run(f"{subsection_name}: Not applicable").bold = True
continue
# Add subsection heading
doc.add_heading(subsection_name, 2)
# Add score with color-coded indicator
if "score" in subsection_data:
score = subsection_data["score"]
score_para = doc.add_paragraph()
score_run = score_para.add_run(f"Score: {score}/10")
score_run.bold = True
# Color code: Red (<5), Yellow (5-7), Green (>7)
if score < 5:
score_run.font.color.rgb = RGBColor(192, 0, 0) # Dark red
elif score < 7:
score_run.font.color.rgb = RGBColor(255, 192, 0) # Orange
else:
score_run.font.color.rgb = RGBColor(0, 176, 80) # Green
# Add section summary
if "summary" in subsection_data:
summary_para = doc.add_paragraph()
summary_para.add_run("Summary:").bold = True
summary_para.add_run(f"\n{subsection_data['summary']}")
# Add remarks
remarks = subsection_data.get("remarks", [])
if remarks:
doc.add_heading("Detailed Feedback", 3)
for i, remark in enumerate(remarks, 1):
# Add remark number
remark_para = doc.add_paragraph()
remark_para.add_run(f"Remark {i}").bold = True
# Add issue
issue_para = doc.add_paragraph()
issue_para.add_run("Issue: ").bold = True
issue_para.add_run(remark["remark"])
# Add original text
original_para = doc.add_paragraph()
original_para.add_run("Original Text: ").bold = True
original_para.add_run(remark["original_text"])
# Add improved version
improved_para = doc.add_paragraph()
improved_para.add_run("Improved Version: ").bold = True
improved_para.add_run(remark["improved_version"])
# Add explanation
explanation_para = doc.add_paragraph()
explanation_para.add_run("Explanation: ").bold = True
explanation_para.add_run(remark["explanation"])
doc.add_paragraph() # Add space between remarks
else:
doc.add_paragraph("No specific remarks for this section.")
doc.add_paragraph() # Add space between subsections
doc.add_page_break() # Add page break between major sections
# Save document
doc.save(doc_path)
return doc_path
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"report_generation_score": 0,
"doc_path": None,
"analysis": None,
"summary": f"Error in report generation: {error_message}",
"error": True
}

93
Backup/V5_multi_agent2/src/reviewer_agents/rigor/R1_originality_contribution_agent.py
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@@ -1,93 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class OriginalityContributionAgent(BaseReviewerAgent):
"""Agent responsible for assessing research novelty and unique contributions."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R1_Originality_Contribution_Agent"
self.category = "Scientific Rigor"
def analyze_originality_contribution(self, text: str, field_context: Dict[str, Any]) -> Dict[str, Any]:
"""Analyzes the originality and contribution of the research."""
prompt = f"""Analyze the following text for originality and contribution to the field. Focus on:
1. Novelty of the research approach
2. Unique contributions to the field
3. Verification of stated novelty claims
4. Comparison with existing literature
5. Advancement of knowledge
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Clarity of novelty statement, contribution highlights
- Introduction: Research gap identification, novelty claims
- Literature Review: Comparison with existing work, gap analysis
- Methodology: Novel approach description, innovation details
- Results: Contribution presentation, advancement demonstration
- Discussion: Impact assessment, future implications
- Conclusion: Contribution summary, field advancement
Text to analyze: {text}
Field context: {json.dumps(field_context, indent=2)}
Provide a detailed analysis in the following JSON format:
{{
"originality_contribution_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "novelty", "contribution", "verification", "comparison", "advancement"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects the research validity
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion"
"focus": str # "novelty", "contribution", "verification", "comparison", "advancement"
}}],
"detailed_feedback": {{
"novelty_assessment": str, # Detailed paragraph about research novelty
"contribution_analysis": str, # Detailed paragraph about contributions
"verification_status": str, # Detailed paragraph about novelty claims
"comparative_analysis": str, # Detailed paragraph about literature comparison
"advancement_evaluation": str # Detailed paragraph about knowledge advancement
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the research.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing originality and contribution: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"originality_contribution_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"novelty_assessment": "",
"contribution_analysis": "",
"verification_status": "",
"comparative_analysis": "",
"advancement_evaluation": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

93
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class ImpactSignificanceAgent(BaseReviewerAgent):
"""Agent responsible for evaluating research impact and significance."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R2_Impact_Significance_Agent"
self.category = "Scientific Rigor"
def analyze_impact_significance(self, text: str, field_context: Dict[str, Any]) -> Dict[str, Any]:
"""Analyzes the impact and significance of the research."""
prompt = f"""Analyze the following text for impact and significance. Focus on:
1. Potential influence on the field
2. Broader implications of findings
3. Influence on future research
4. Practical applications
5. Policy implications
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Impact statement, significance highlights
- Introduction: Research importance, field relevance
- Literature Review: Gap impact, field advancement
- Methodology: Innovation potential, scalability
- Results: Key findings impact, practical value
- Discussion: Broader implications, future directions
- Conclusion: Impact summary, application potential
Text to analyze: {text}
Field context: {json.dumps(field_context, indent=2)}
Provide a detailed analysis in the following JSON format:
{{
"impact_significance_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "field_influence", "implications", "future_research", "applications", "policy"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects the research significance
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion"
"focus": str # "field_influence", "implications", "future_research", "applications", "policy"
}}],
"detailed_feedback": {{
"field_influence": str, # Detailed paragraph about field influence
"broader_implications": str, # Detailed paragraph about implications
"future_research_impact": str, # Detailed paragraph about future research
"practical_applications": str, # Detailed paragraph about applications
"policy_implications": str # Detailed paragraph about policy
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the research impact and significance.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing impact and significance: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"impact_significance_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"field_influence": "",
"broader_implications": "",
"future_research_impact": "",
"practical_applications": "",
"policy_implications": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

94
Backup/V5_multi_agent2/src/reviewer_agents/rigor/R3_ethics_compliance_agent.py
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class EthicsComplianceAgent(BaseReviewerAgent):
"""Agent responsible for reviewing ethical considerations and research standards."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R3_Ethics_Compliance_Agent"
self.category = "Scientific Rigor"
def analyze_ethics_compliance(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes ethical considerations and compliance with research standards."""
prompt = f"""Analyze the following text for ethical considerations and research standards compliance. Focus on:
1. Conflicts of interest
2. Data privacy and protection
3. Informed consent procedures
4. Research integrity
5. Adherence to ethical guidelines
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Ethical statement, compliance summary
- Introduction: Research ethics framework, compliance approach
- Methodology: Ethical procedures, consent process
- Data Collection: Privacy measures, protection protocols
- Analysis: Integrity measures, bias prevention
- Results: Ethical presentation, privacy maintenance
- Discussion: Ethical implications, compliance reflection
- Conclusion: Ethical summary, compliance assurance
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"ethics_compliance_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "conflicts", "privacy", "consent", "integrity", "guidelines"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects ethical compliance
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "methodology", "data_collection", "analysis", "results", "discussion", "conclusion"
"focus": str # "conflicts", "privacy", "consent", "integrity", "guidelines"
}}],
"detailed_feedback": {{
"conflicts_assessment": str, # Detailed paragraph about conflicts of interest
"privacy_compliance": str, # Detailed paragraph about data privacy
"consent_procedures": str, # Detailed paragraph about informed consent
"research_integrity": str, # Detailed paragraph about research integrity
"guidelines_adherence": str # Detailed paragraph about ethical guidelines
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances ethical compliance and research standards.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing ethics and compliance: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"ethics_compliance_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"conflicts_assessment": "",
"privacy_compliance": "",
"consent_procedures": "",
"research_integrity": "",
"guidelines_adherence": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

94
Backup/V5_multi_agent2/src/reviewer_agents/rigor/R4_data_code_availability_agent.py
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class DataCodeAvailabilityAgent(BaseReviewerAgent):
"""Agent responsible for evaluating data and code availability."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R4_Data_Code_Availability_Agent"
self.category = "Scientific Rigor"
def analyze_data_code_availability(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes data and code availability."""
prompt = f"""Analyze the following text for data and code availability. Focus on:
1. Data sharing practices
2. Code repository availability
3. Documentation completeness
4. Access restrictions justification
5. Reproducibility support
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Data/code availability statement
- Introduction: Research transparency approach
- Methodology: Data collection details, code implementation
- Data Description: Dataset structure, access methods
- Code Documentation: Implementation details, usage instructions
- Results: Data presentation, code results
- Discussion: Reproducibility considerations
- Conclusion: Availability summary, access information
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"data_code_availability_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "data_sharing", "code_availability", "documentation", "restrictions", "reproducibility"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects research transparency
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "methodology", "data_description", "code_documentation", "results", "discussion", "conclusion"
"focus": str # "data_sharing", "code_availability", "documentation", "restrictions", "reproducibility"
}}],
"detailed_feedback": {{
"data_sharing_assessment": str, # Detailed paragraph about data sharing
"code_availability": str, # Detailed paragraph about code availability
"documentation_completeness": str, # Detailed paragraph about documentation
"restrictions_justification": str, # Detailed paragraph about access restrictions
"reproducibility_support": str # Detailed paragraph about reproducibility
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances data and code availability.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing data and code availability: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"data_code_availability_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"data_sharing_assessment": "",
"code_availability": "",
"documentation_completeness": "",
"restrictions_justification": "",
"reproducibility_support": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

109
Backup/V5_multi_agent2/src/reviewer_agents/rigor/R5_statistical_rigor_agent.py
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class StatisticalRigorAgent(BaseReviewerAgent):
"""Agent responsible for evaluating statistical methods appropriateness and correctness."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R5_Statistical_Rigor_Agent"
self.category = "Scientific Rigor"
def analyze_statistical_rigor(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes statistical methods appropriateness and correctness."""
prompt = f"""Analyze the following text for statistical methods appropriateness and correctness. Focus on:
1. Statistical test selection
2. Assumption verification
3. Sample size justification
4. Multiple comparison handling
5. Effect size reporting
6. Confidence intervals
7. P-value interpretation
8. Statistical power
9. Missing data handling
10. Outlier treatment
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Statistical approach summary
- Introduction: Statistical framework overview
- Methodology: Statistical methods description
- Data Preparation: Assumption checks, data cleaning
- Analysis: Statistical test implementation
- Results: Statistical findings presentation
- Discussion: Statistical interpretation
- Conclusion: Statistical significance summary
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"statistical_rigor_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "test_selection", "assumptions", "sample_size", "multiple_comparisons", "effect_size", "confidence_intervals", "p_value", "power", "missing_data", "outliers"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects statistical validity
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "methodology", "data_preparation", "analysis", "results", "discussion", "conclusion"
"focus": str # "test_selection", "assumptions", "sample_size", "multiple_comparisons", "effect_size", "confidence_intervals", "p_value", "power", "missing_data", "outliers"
}}],
"detailed_feedback": {{
"test_selection": str, # Detailed paragraph about statistical test selection
"assumption_verification": str, # Detailed paragraph about assumption verification
"sample_size_justification": str, # Detailed paragraph about sample size
"multiple_comparison_handling": str, # Detailed paragraph about multiple comparisons
"effect_size_reporting": str, # Detailed paragraph about effect size
"confidence_intervals": str, # Detailed paragraph about confidence intervals
"p_value_interpretation": str, # Detailed paragraph about p-value interpretation
"statistical_power": str, # Detailed paragraph about statistical power
"missing_data_handling": str, # Detailed paragraph about missing data
"outlier_treatment": str # Detailed paragraph about outlier treatment
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances statistical rigor.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing statistical rigor: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"statistical_rigor_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"test_selection": "",
"assumption_verification": "",
"sample_size_justification": "",
"multiple_comparison_handling": "",
"effect_size_reporting": "",
"confidence_intervals": "",
"p_value_interpretation": "",
"statistical_power": "",
"missing_data_handling": "",
"outlier_treatment": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

110
Backup/V5_multi_agent2/src/reviewer_agents/rigor/R6_technical_accuracy_agent.py
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@@ -1,110 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class TechnicalAccuracyAgent(BaseReviewerAgent):
"""Agent responsible for reviewing mathematical derivations, algorithms, and technical content."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R6_Technical_Accuracy_Agent"
self.category = "Scientific Rigor"
def analyze_technical_accuracy(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes mathematical derivations, algorithms, and technical content."""
prompt = f"""Analyze the following text for technical accuracy. Focus on:
1. Mathematical derivation correctness
2. Algorithm correctness and efficiency
3. Technical terminology accuracy
4. Equation clarity and presentation
5. Technical content completeness
6. Logical consistency
7. Implementation details
8. Edge case handling
9. Complexity analysis
10. Technical documentation
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Technical approach summary
- Introduction: Technical framework overview
- Methodology: Technical methods description
- Mathematical Framework: Derivation presentation
- Algorithm Description: Implementation details
- Technical Analysis: Complexity and efficiency
- Results: Technical findings presentation
- Discussion: Technical implications
- Conclusion: Technical significance summary
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"technical_accuracy_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "derivations", "algorithms", "terminology", "equations", "completeness", "consistency", "implementation", "edge_cases", "complexity", "documentation"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects technical accuracy
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "methodology", "mathematical_framework", "algorithm_description", "technical_analysis", "results", "discussion", "conclusion"
"focus": str # "derivations", "algorithms", "terminology", "equations", "completeness", "consistency", "implementation", "edge_cases", "complexity", "documentation"
}}],
"detailed_feedback": {{
"derivation_correctness": str, # Detailed paragraph about mathematical derivations
"algorithm_accuracy": str, # Detailed paragraph about algorithm correctness
"terminology_accuracy": str, # Detailed paragraph about technical terminology
"equation_clarity": str, # Detailed paragraph about equation presentation
"content_completeness": str, # Detailed paragraph about technical content
"logical_consistency": str, # Detailed paragraph about logical consistency
"implementation_details": str, # Detailed paragraph about implementation
"edge_case_handling": str, # Detailed paragraph about edge cases
"complexity_analysis": str, # Detailed paragraph about complexity
"technical_documentation": str # Detailed paragraph about documentation
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances technical accuracy.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing technical accuracy: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"technical_accuracy_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"derivation_correctness": "",
"algorithm_accuracy": "",
"terminology_accuracy": "",
"equation_clarity": "",
"content_completeness": "",
"logical_consistency": "",
"implementation_details": "",
"edge_case_handling": "",
"complexity_analysis": "",
"technical_documentation": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

110
Backup/V5_multi_agent2/src/reviewer_agents/rigor/R7_consistency_agent.py
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@@ -1,110 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class ConsistencyAgent(BaseReviewerAgent):
"""Agent responsible for checking logical coherence across sections."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "R7_Consistency_Agent"
self.category = "Scientific Rigor"
def analyze_consistency(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes logical coherence across sections."""
prompt = f"""Analyze the following text for logical coherence and consistency across sections. Focus on:
1. Alignment between methods and results
2. Consistency between results and conclusions
3. Logical flow between sections
4. Terminology consistency
5. Hypothesis-testing alignment
6. Data interpretation consistency
7. Citation consistency
8. Figure-text alignment
9. Table-text alignment
10. Supplementary material consistency
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Consistency with main text
- Introduction: Alignment with methodology
- Literature Review: Citation consistency
- Methodology: Methods-results alignment
- Results: Results-conclusions alignment
- Discussion: Interpretation consistency
- Conclusion: Overall coherence
- Figures/Tables: Text alignment
- Supplementary: Main text consistency
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"consistency_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "methods_results", "results_conclusions", "logical_flow", "terminology", "hypothesis", "interpretation", "citations", "figures", "tables", "supplementary"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects logical coherence
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "figures_tables", "supplementary"
"focus": str # "methods_results", "results_conclusions", "logical_flow", "terminology", "hypothesis", "interpretation", "citations", "figures", "tables", "supplementary"
}}],
"detailed_feedback": {{
"methods_results_alignment": str, # Detailed paragraph about methods-results alignment
"results_conclusions_alignment": str, # Detailed paragraph about results-conclusions alignment
"logical_flow": str, # Detailed paragraph about logical flow
"terminology_consistency": str, # Detailed paragraph about terminology consistency
"hypothesis_testing": str, # Detailed paragraph about hypothesis-testing alignment
"interpretation_consistency": str, # Detailed paragraph about interpretation consistency
"citation_consistency": str, # Detailed paragraph about citation consistency
"figure_text_alignment": str, # Detailed paragraph about figure-text alignment
"table_text_alignment": str, # Detailed paragraph about table-text alignment
"supplementary_consistency": str # Detailed paragraph about supplementary material consistency
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances logical coherence and consistency.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing consistency: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"consistency_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"methods_results_alignment": "",
"results_conclusions_alignment": "",
"logical_flow": "",
"terminology_consistency": "",
"hypothesis_testing": "",
"interpretation_consistency": "",
"citation_consistency": "",
"figure_text_alignment": "",
"table_text_alignment": "",
"supplementary_consistency": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

82
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@@ -1,82 +0,0 @@
# Scientific Rigor Agents
These agents ensure research meets high standards of originality, ethics, and accuracy by evaluating various aspects of scientific rigor.
## Agent Labels and Descriptions
### R1 - Originality and Contribution Agent
- **Purpose**: Assesses research novelty and unique contributions to the field.
- **Key Evaluations**:
- Novelty of research approach
- Unique contributions
- Verification of novelty claims
- Comparison with existing literature
- Knowledge advancement
### R2 - Impact and Significance Agent
- **Purpose**: Evaluates research influence and broader implications.
- **Key Evaluations**:
- Field influence potential
- Research advancement potential
- Practical applications
- Policy implications
- Future research directions
### R3 - Ethics and Compliance Agent
- **Purpose**: Reviews ethical considerations and research standards compliance.
- **Key Evaluations**:
- Conflicts of interest
- Data privacy and protection
- Informed consent procedures
- Research integrity
- Ethical guidelines adherence
### R4 - Data and Code Availability Agent
- **Purpose**: Checks data and code sharing practices and documentation.
- **Key Evaluations**:
- Data availability
- Code availability
- Documentation quality
- Reproducibility
- Sharing practices
### R5 - Statistical Rigor Agent
- **Purpose**: Ensures appropriateness and correctness of statistical methods.
- **Key Evaluations**:
- Method appropriateness
- Analysis correctness
- Assumptions validation
- Power analysis
- Reporting completeness
### R6 - Technical Accuracy Agent
- **Purpose**: Reviews mathematical derivations and technical content.
- **Key Evaluations**:
- Mathematical correctness
- Algorithm accuracy
- Technical clarity
- Derivation completeness
- Implementation feasibility
### R7 - Consistency Agent
- **Purpose**: Checks logical coherence across manuscript sections.
- **Key Evaluations**:
- Methods-Results alignment
- Results-Conclusions alignment
- Claims consistency
- Variable/terminology consistency
- Cross-section coherence
## Implementation Details
Each agent:
- Inherits from `BaseReviewerAgent`
- Uses the `ReportTemplate` for standardized reports
- Provides detailed analysis with subscores
- Generates specific improvement suggestions
- Includes error handling and reporting
- Returns structured JSON output
## Usage
The agents can be used individually or as part of a comprehensive review process. Each agent focuses on specific aspects of scientific rigor while maintaining consistency in reporting format and evaluation standards.

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Backup/V5_multi_agent2/src/reviewer_agents/rigor/__init__.py
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"""
This package contains the rigor agents for manuscript analysis.
"""

95
Backup/V5_multi_agent2/src/reviewer_agents/section/S10_supplementary_materials_agent.py
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class SupplementaryMaterialsAgentS10(BaseReviewerAgent):
"""Agent responsible for evaluating the supplementary materials of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S10_Supplementary_Materials_Agent"
self.category = "Section Review"
def analyze_supplementary_materials(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the supplementary materials of the manuscript."""
prompt = f"""Analyze the following supplementary materials for quality and completeness. Focus on:
1. Relevance to main text
2. Clarity of presentation
3. Consistency with main text
4. Completeness of information
5. Organization and structure
6. Data presentation
7. Methodological details
8. Additional results
9. Reference to main text
10. Accessibility and usability
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Relevance: Connection to main text, value addition
- Clarity: Presentation, organization, accessibility
- Consistency: Alignment with main text, coherence
- Completeness: Information detail, methodological thoroughness
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "relevance", "clarity", "consistency", "completeness"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "relevance", "clarity", "consistency", "completeness"
"focus": str # "connection", "presentation", "organization", "accessibility", "alignment", "coherence", "detail", "thoroughness"
}}],
"detailed_feedback": {{
"relevance_analysis": str, # Detailed paragraph about relevance to main text
"clarity_analysis": str, # Detailed paragraph about presentation clarity
"consistency_analysis": str, # Detailed paragraph about consistency with main text
"completeness_analysis": str, # Detailed paragraph about information completeness
"organization_analysis": str # Detailed paragraph about structure and organization
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the supplementary materials.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing supplementary materials: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"relevance_analysis": "",
"clarity_analysis": "",
"consistency_analysis": "",
"completeness_analysis": "",
"organization_analysis": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

113
Backup/V5_multi_agent2/src/reviewer_agents/section/S1_title_keywords_agent.py
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class TitleKeywordsAgentS1(BaseReviewerAgent):
"""Agent responsible for evaluating the title and keywords of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S1_Title_Keywords_Agent"
self.category = "Section Review"
def analyze_title_keywords(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the title and keywords of the manuscript."""
prompt = f"""Analyze the title and keywords section of the manuscript. Follow these steps:
1. FIRST, check if there is a dedicated "Keywords:" or "Keywords" section in the text.
- Look for a line that starts with "Keywords:" or "Keywords"
- If no such section is found, set has_keywords = false
- If found, set has_keywords = true and extract the keywords
2. For the title analysis:
- Analyze the current title considering ALL aspects simultaneously:
* Clarity: Is it clear and understandable?
* Accuracy: Does it accurately represent the content?
* Impact: Does it capture attention and significance?
* SEO: Is it optimized for search engines?
* Standards: Does it follow field conventions?
- Generate ONE comprehensive improvement suggestion that addresses all these aspects
- The improved title should be the optimal balance of all these factors
3. For keywords analysis (ONLY if has_keywords = true):
- Analyze relevance, coverage, and specificity
- Provide improvement suggestions
- Consider search engine optimization
4. If has_keywords = false:
- Set all keyword-related fields to empty or null
- Do not generate any keyword-related feedback
- Do not make assumptions about keywords from other text
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"title_keywords_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "title_clarity", "title_length", "keywords_relevance", "keywords_coverage", "guidelines", "discoverability"
"location": str, # "Title" or "Keywords"
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The current title
"improved_version": str, # ONE comprehensive improved title that balances all aspects
"explanation": str, # Detailed explanation of how the improved title addresses clarity, accuracy, impact, SEO, and standards
"location": str, # "Title"
"category": str, # "title"
"focus": str # "comprehensive_improvement"
}}],
"detailed_feedback": {{
"title_analysis": str, # Detailed paragraph about title quality
"keywords_analysis": str, # "No keywords section found" if has_keywords = false
"guidelines_compliance": str, # Detailed paragraph about field conventions
"discoverability_assessment": str, # Detailed paragraph about search optimization
"audience_alignment": str # Detailed paragraph about appeal and significance
}},
"summary": str # Overall assessment paragraph
}}
Important:
1. ONLY analyze the title and keywords section
2. If no "Keywords:" or "Keywords" section is found:
- Set keywords_analysis to "No keywords section found"
- Do not include any keyword-related critical remarks
- Do not include any keyword-related improvement suggestions
- Do not make assumptions about keywords from other text
3. Generate ONE comprehensive title improvement that considers all aspects simultaneously
4. The title improvement should balance clarity, accuracy, impact, SEO, and standards
5. All locations should be either "Title" or "Keywords", never "Abstract"
6. Focus on improving discoverability and search optimization
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing title and keywords: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"title_keywords_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"title_analysis": "",
"keywords_analysis": "",
"guidelines_compliance": "",
"discoverability_assessment": "",
"audience_alignment": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

96
Backup/V5_multi_agent2/src/reviewer_agents/section/S2_abstract_agent.py
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from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class AbstractAgentS2(BaseReviewerAgent):
"""Agent responsible for evaluating the abstract of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S2_Abstract_Agent"
self.category = "Section Review"
def analyze_abstract(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the abstract of the manuscript."""
prompt = f"""Analyze the following abstract for quality and completeness. Focus on:
1. Structure and organization
2. Content completeness
3. Clarity and readability
4. Methodology description
5. Results presentation
6. Conclusion strength
7. Scientific writing standards
8. Field-specific requirements
9. Impact communication
10. Technical accuracy
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Structure: Organization, flow, section presence
- Content: Completeness, accuracy, technical details
- Clarity: Language, readability, technical terms
- Standards: Scientific writing, field conventions
- Impact: Significance, implications, contributions
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "structure", "content", "clarity", "standards", "impact"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "structure", "content", "clarity", "standards", "impact"
"focus": str # "organization", "completeness", "readability", "methodology", "results", "conclusion"
}}],
"detailed_feedback": {{
"structure_analysis": str, # Detailed paragraph about abstract structure
"content_analysis": str, # Detailed paragraph about content completeness
"clarity_assessment": str, # Detailed paragraph about readability
"standards_compliance": str, # Detailed paragraph about scientific standards
"impact_evaluation": str # Detailed paragraph about significance
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the abstract.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing abstract: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"structure_analysis": "",
"content_analysis": "",
"clarity_assessment": "",
"standards_compliance": "",
"impact_evaluation": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

96
Backup/V5_multi_agent2/src/reviewer_agents/section/S3_introduction_agent.py
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@@ -1,96 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class IntroductionAgentS3(BaseReviewerAgent):
"""Agent responsible for evaluating the introduction of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S3_Introduction_Agent"
self.category = "Section Review"
def analyze_introduction(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the introduction of the manuscript."""
prompt = f"""Analyze the following introduction for quality and effectiveness. Focus on:
1. Background context
2. Problem statement
3. Research gap identification
4. Objectives clarity
5. Significance justification
6. Literature integration
7. Flow and organization
8. Technical accuracy
9. Research scope
10. Hypothesis/questions
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Context: Background, field overview
- Problem: Issue identification, gap analysis
- Objectives: Goals, research questions
- Significance: Impact, contribution
- Structure: Organization, flow
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "context", "problem", "objectives", "significance", "structure"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "context", "problem", "objectives", "significance", "structure"
"focus": str # "background", "problem", "gap", "objectives", "significance", "flow"
}}],
"detailed_feedback": {{
"context_analysis": str, # Detailed paragraph about background
"problem_analysis": str, # Detailed paragraph about problem statement
"objectives_analysis": str, # Detailed paragraph about research goals
"significance_assessment": str, # Detailed paragraph about impact
"structure_evaluation": str # Detailed paragraph about organization
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the introduction.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing introduction: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"context_analysis": "",
"problem_analysis": "",
"objectives_analysis": "",
"significance_assessment": "",
"structure_evaluation": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

96
Backup/V5_multi_agent2/src/reviewer_agents/section/S4_literature_review_agent.py
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@@ -1,96 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class LiteratureReviewAgentS4(BaseReviewerAgent):
"""Agent responsible for evaluating the literature review of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S4_Literature_Review_Agent"
self.category = "Section Review"
def analyze_literature_review(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the literature review of the manuscript."""
prompt = f"""Analyze the following literature review for quality and comprehensiveness. Focus on:
1. Coverage breadth
2. Historical context
3. Current state
4. Critical analysis
5. Gap identification
6. Theoretical framework
7. Methodological review
8. Citation quality
9. Organization logic
10. Synthesis depth
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Coverage: Breadth, depth, relevance
- Analysis: Critical thinking, synthesis
- Structure: Organization, flow
- Citations: Quality, recency
- Integration: Connection to research
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "coverage", "analysis", "structure", "citations", "integration"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "coverage", "analysis", "structure", "citations", "integration"
"focus": str # "breadth", "depth", "synthesis", "organization", "relevance"
}}],
"detailed_feedback": {{
"coverage_analysis": str, # Detailed paragraph about literature coverage
"analysis_quality": str, # Detailed paragraph about critical analysis
"structure_evaluation": str, # Detailed paragraph about organization
"citation_assessment": str, # Detailed paragraph about citation quality
"integration_review": str # Detailed paragraph about research connection
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the literature review.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing literature review: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"coverage_analysis": "",
"analysis_quality": "",
"structure_evaluation": "",
"citation_assessment": "",
"integration_review": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

96
Backup/V5_multi_agent2/src/reviewer_agents/section/S5_methodology_agent.py
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@@ -1,96 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class MethodologyAgentS5(BaseReviewerAgent):
"""Agent responsible for evaluating the methodology of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S5_Methodology_Agent"
self.category = "Section Review"
def analyze_methodology(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the methodology of the manuscript."""
prompt = f"""Analyze the following methodology for quality and completeness. Focus on:
1. Research design
2. Data collection
3. Sampling approach
4. Instrumentation
5. Procedures
6. Analysis methods
7. Validity measures
8. Reliability assessment
9. Ethical considerations
10. Limitations handling
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Design: Approach, framework, rationale
- Methods: Techniques, procedures, tools
- Analysis: Statistical methods, qualitative approaches
- Quality: Validity, reliability, rigor
- Ethics: Consent, approval, considerations
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "design", "methods", "analysis", "quality", "ethics"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "design", "methods", "analysis", "quality", "ethics"
"focus": str # "approach", "techniques", "procedures", "validity", "reliability"
}}],
"detailed_feedback": {{
"design_analysis": str, # Detailed paragraph about research design
"methods_assessment": str, # Detailed paragraph about methodology
"analysis_evaluation": str, # Detailed paragraph about analysis approach
"quality_review": str, # Detailed paragraph about validity and reliability
"ethics_compliance": str # Detailed paragraph about ethical considerations
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the methodology.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing methodology: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"design_analysis": "",
"methods_assessment": "",
"analysis_evaluation": "",
"quality_review": "",
"ethics_compliance": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

96
Backup/V5_multi_agent2/src/reviewer_agents/section/S6_results_agent.py
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@@ -1,96 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class ResultsAgentS6(BaseReviewerAgent):
"""Agent responsible for evaluating the results of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S6_Results_Agent"
self.category = "Section Review"
def analyze_results(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the results of the manuscript."""
prompt = f"""Analyze the following results for quality and presentation. Focus on:
1. Data presentation
2. Statistical analysis
3. Figure/table quality
4. Result interpretation
5. Significance reporting
6. Effect sizes
7. Confidence intervals
8. Statistical tests
9. Data visualization
10. Result organization
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Presentation: Clarity, organization, visualization
- Analysis: Statistical methods, significance
- Interpretation: Meaning, implications
- Quality: Accuracy, completeness
- Impact: Significance, effect sizes
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "presentation", "analysis", "interpretation", "quality", "impact"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "presentation", "analysis", "interpretation", "quality", "impact"
"focus": str # "clarity", "statistics", "visualization", "interpretation", "significance"
}}],
"detailed_feedback": {{
"presentation_analysis": str, # Detailed paragraph about data presentation
"analysis_quality": str, # Detailed paragraph about statistical analysis
"interpretation_review": str, # Detailed paragraph about result interpretation
"visualization_assessment": str, # Detailed paragraph about figures/tables
"significance_evaluation": str # Detailed paragraph about statistical significance
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the results section.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing results: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"presentation_analysis": "",
"analysis_quality": "",
"interpretation_review": "",
"visualization_assessment": "",
"significance_evaluation": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

96
Backup/V5_multi_agent2/src/reviewer_agents/section/S7_discussion_agent.py
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@@ -1,96 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class DiscussionAgentS7(BaseReviewerAgent):
"""Agent responsible for evaluating the discussion of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S7_Discussion_Agent"
self.category = "Section Review"
def analyze_discussion(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the discussion of the manuscript."""
prompt = f"""Analyze the following discussion for quality and completeness. Focus on:
1. Result interpretation
2. Literature comparison
3. Limitation analysis
4. Future work
5. Practical implications
6. Theoretical contributions
7. Research gap addressing
8. Methodology reflection
9. Result significance
10. Conclusion alignment
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Interpretation: Result analysis, significance
- Context: Literature comparison, research gaps
- Reflection: Limitations, future work
- Impact: Practical implications, theoretical contributions
- Quality: Completeness, coherence
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "interpretation", "context", "reflection", "impact", "quality"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "interpretation", "context", "reflection", "impact", "quality"
"focus": str # "interpretation", "comparison", "limitations", "implications", "significance"
}}],
"detailed_feedback": {{
"interpretation_analysis": str, # Detailed paragraph about result interpretation
"context_review": str, # Detailed paragraph about literature comparison
"reflection_assessment": str, # Detailed paragraph about limitations/future work
"impact_evaluation": str, # Detailed paragraph about practical/theoretical impact
"quality_analysis": str # Detailed paragraph about overall discussion quality
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the discussion.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing discussion: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"interpretation_analysis": "",
"context_review": "",
"reflection_assessment": "",
"impact_evaluation": "",
"quality_analysis": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

95
Backup/V5_multi_agent2/src/reviewer_agents/section/S8_conclusion_agent.py
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@@ -1,95 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class ConclusionAgentS8(BaseReviewerAgent):
"""Agent responsible for evaluating the conclusion of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S8_Conclusion_Agent"
self.category = "Section Review"
def analyze_conclusion(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the conclusion of the manuscript."""
prompt = f"""Analyze the following conclusion for quality and completeness. Focus on:
1. Support from results
2. Research objective fulfillment
3. Key findings summary
4. Contribution clarity
5. Practical implications
6. Theoretical implications
7. Future research suggestions
8. Final statement strength
9. Avoidance of new information
10. Conciseness and clarity
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Support: Evidence-based, result alignment
- Objectives: Fulfillment, contribution clarity
- Implications: Practical, theoretical, future directions
- Presentation: Clarity, conciseness, strength
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "support", "objectives", "implications", "presentation"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "support", "objectives", "implications", "presentation"
"focus": str # "evidence", "fulfillment", "clarity", "implications", "future_directions", "strength"
}}],
"detailed_feedback": {{
"support_analysis": str, # Detailed paragraph about result support
"objective_fulfillment": str, # Detailed paragraph about research objective fulfillment
"implications_analysis": str, # Detailed paragraph about implications
"presentation_analysis": str, # Detailed paragraph about presentation quality
"contribution_analysis": str # Detailed paragraph about contribution clarity
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the conclusion.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing conclusion: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"support_analysis": "",
"objective_fulfillment": "",
"implications_analysis": "",
"presentation_analysis": "",
"contribution_analysis": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

95
Backup/V5_multi_agent2/src/reviewer_agents/section/S9_references_agent.py
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@@ -1,95 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class ReferencesAgentS9(BaseReviewerAgent):
"""Agent responsible for evaluating the references of a manuscript."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "S9_References_Agent"
self.category = "Section Review"
def analyze_references(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the references of the manuscript."""
prompt = f"""Analyze the following references for quality and completeness. Focus on:
1. Citation accuracy
2. Reference completeness
3. Format consistency
4. Source relevance
5. Source recency
6. Source diversity
7. Citation-text alignment
8. Reference list organization
9. Style guide compliance
10. Cross-reference accuracy
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Accuracy: Citation correctness, cross-reference accuracy
- Completeness: Reference details, source information
- Format: Style compliance, consistency
- Quality: Relevance, recency, diversity
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "accuracy", "completeness", "format", "quality"
"location": str, # Section reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects manuscript quality
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "accuracy", "completeness", "format", "quality"
"focus": str # "citation", "reference", "format", "style", "relevance", "recency", "diversity"
}}],
"detailed_feedback": {{
"accuracy_analysis": str, # Detailed paragraph about citation accuracy
"completeness_analysis": str, # Detailed paragraph about reference completeness
"format_analysis": str, # Detailed paragraph about format consistency
"quality_analysis": str, # Detailed paragraph about source quality
"organization_analysis": str # Detailed paragraph about reference organization
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the references.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing references: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"accuracy_analysis": "",
"completeness_analysis": "",
"format_analysis": "",
"quality_analysis": "",
"organization_analysis": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

109
Backup/V5_multi_agent2/src/reviewer_agents/writing/W1_language_style_agent.py
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@@ -1,109 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class LanguageStyleAgent(BaseReviewerAgent):
"""Agent responsible for reviewing grammar, spelling, and punctuation."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "W1_Language_Style_Agent"
self.category = "Writing and Presentation"
def analyze_language_style(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes grammar, spelling, and punctuation in the text."""
prompt = f"""Analyze the following text for grammar, spelling, and punctuation issues. Focus on:
1. Grammar correctness
2. Spelling accuracy
3. Punctuation usage
4. Sentence structure
5. Verb tense consistency
6. Subject-verb agreement
7. Article usage
8. Preposition usage
9. Conjunction usage
10. Academic writing conventions
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Conciseness and clarity
- Introduction: Academic tone and flow
- Literature Review: Citation language
- Methodology: Technical description
- Results: Data presentation
- Discussion: Argument structure
- Conclusion: Summary language
- References: Citation format
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"language_style_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "grammar", "spelling", "punctuation", "sentence_structure", "verb_tense", "subject_verb", "articles", "prepositions", "conjunctions", "academic_conventions"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects readability
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "references"
"focus": str # "grammar", "spelling", "punctuation", "sentence_structure", "verb_tense", "subject_verb", "articles", "prepositions", "conjunctions", "academic_conventions"
}}],
"detailed_feedback": {{
"grammar_correctness": str, # Detailed paragraph about grammar issues
"spelling_accuracy": str, # Detailed paragraph about spelling issues
"punctuation_usage": str, # Detailed paragraph about punctuation issues
"sentence_structure": str, # Detailed paragraph about sentence structure
"verb_tense_consistency": str, # Detailed paragraph about verb tense consistency
"subject_verb_agreement": str, # Detailed paragraph about subject-verb agreement
"article_usage": str, # Detailed paragraph about article usage
"preposition_usage": str, # Detailed paragraph about preposition usage
"conjunction_usage": str, # Detailed paragraph about conjunction usage
"academic_conventions": str # Detailed paragraph about academic writing conventions
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the language and style.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing language style: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"language_style_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"grammar_correctness": "",
"spelling_accuracy": "",
"punctuation_usage": "",
"sentence_structure": "",
"verb_tense_consistency": "",
"subject_verb_agreement": "",
"article_usage": "",
"preposition_usage": "",
"conjunction_usage": "",
"academic_conventions": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

109
Backup/V5_multi_agent2/src/reviewer_agents/writing/W2_narrative_structure_agent.py
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@@ -1,109 +0,0 @@
from typing import Dict, Any, List
import json
from ...core.base_agent import BaseReviewerAgent
from ...core.report_template import ReportTemplate
class NarrativeStructureAgent(BaseReviewerAgent):
"""Agent responsible for evaluating the overall flow, coherence, and logical organization of the paper."""
def __init__(self, model="gpt-4.1-nano"):
super().__init__(model)
self.name = "W2_Narrative_Structure_Agent"
self.category = "Writing and Presentation"
def analyze_narrative_structure(self, text: str, research_type: str) -> Dict[str, Any]:
"""Analyzes the narrative flow and structural organization of the text."""
prompt = f"""Analyze the following text for narrative flow and structural organization. Focus on:
1. Overall narrative coherence
2. Logical progression of ideas
3. Section transitions
4. Paragraph organization
5. Topic sentence effectiveness
6. Supporting evidence integration
7. Conclusion alignment with introduction
8. Research question/hypothesis tracking
9. Visual element integration
10. Reader engagement
For each section, provide at least 2-3 improvement suggestions. Consider these categories:
- Abstract: Research narrative overview
- Introduction: Research context and flow
- Literature Review: Evidence synthesis
- Methodology: Process description
- Results: Finding presentation
- Discussion: Argument development
- Conclusion: Research story closure
- Figures/Tables: Visual narrative
Text to analyze: {text}
Research type: {research_type}
Provide a detailed analysis in the following JSON format:
{{
"narrative_structure_score": int, # Single comprehensive score (1-10)
"critical_remarks": [{{
"category": str, # "narrative_coherence", "logical_progression", "transitions", "paragraph_organization", "topic_sentences", "evidence_integration", "conclusion_alignment", "hypothesis_tracking", "visual_integration", "reader_engagement"
"location": str, # Section/paragraph reference
"issue": str, # Detailed description of the issue
"severity": str, # "high", "medium", "low"
"impact": str # How this affects the narrative
}}],
"improvement_suggestions": [{{
"original_text": str, # The problematic text
"improved_version": str, # AI-generated improvement
"explanation": str, # Why this improvement helps
"location": str, # Where to apply this change
"category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "figures_tables"
"focus": str # "narrative_coherence", "logical_progression", "transitions", "paragraph_organization", "topic_sentences", "evidence_integration", "conclusion_alignment", "hypothesis_tracking", "visual_integration", "reader_engagement"
}}],
"detailed_feedback": {{
"narrative_coherence": str, # Detailed paragraph about narrative coherence
"logical_progression": str, # Detailed paragraph about logical progression
"section_transitions": str, # Detailed paragraph about section transitions
"paragraph_organization": str, # Detailed paragraph about paragraph organization
"topic_sentence_effectiveness": str, # Detailed paragraph about topic sentence effectiveness
"supporting_evidence_integration": str, # Detailed paragraph about supporting evidence integration
"conclusion_alignment": str, # Detailed paragraph about conclusion alignment
"hypothesis_tracking": str, # Detailed paragraph about hypothesis tracking
"visual_element_integration": str, # Detailed paragraph about visual element integration
"reader_engagement": str # Detailed paragraph about reader engagement
}},
"summary": str # Overall assessment paragraph
}}
Important: Generate at least 10-15 improvement suggestions across different sections and categories.
Each suggestion should be specific, actionable, and include clear explanations of how it enhances the narrative structure.
"""
try:
response = self.llm(prompt)
analysis = json.loads(response)
return analysis
except Exception as e:
return self._generate_error_report(f"Error analyzing narrative structure: {str(e)}")
def _generate_error_report(self, error_message: str) -> Dict[str, Any]:
"""Generates a structured error report."""
return {
"narrative_structure_score": 0,
"critical_remarks": [],
"improvement_suggestions": [],
"detailed_feedback": {
"narrative_coherence": "",
"logical_progression": "",
"section_transitions": "",
"paragraph_organization": "",
"topic_sentence_effectiveness": "",
"supporting_evidence_integration": "",
"conclusion_alignment": "",
"hypothesis_tracking": "",
"visual_element_integration": "",
"reader_engagement": ""
},
"summary": f"Error in analysis: {error_message}",
"error": True
}

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