grant-mock-reviewer（授予模拟审稿人）

Grant Mock Reviewer

A simulated NIH study section reviewer that provides structured, rigorous critique of grant proposals using the official NIH scoring criteria and methodology.

Capabilities

1. NIH Scoring Rubric Application: Official 1-9 scale scoring across all 5 criteria
2. Weakness Identification: Systematic detection of common proposal flaws
3. Critique Generation: Structured written critiques for each review criterion
4. Summary Statement: Complete mock Summary Statement output
5. Revision Guidance: Prioritized, actionable recommendations for improvement

Usage

Command Line

# Full mock review with Summary Statement
python3 scripts/main.py --input proposal.pdf --format pdf --output review.md

# Review Specific Aims only
python3 scripts/main.py --input aims.pdf --section aims --output aims_review.md

# Targeted review (specific criterion focus)
python3 scripts/main.py --input proposal.pdf --focus approach --output approach_critique.md

# Generate NIH-style scores only
python3 scripts/main.py --input proposal.pdf --scores-only --output scores.json

# Compare before/after revision
python3 scripts/main.py --original original.pdf --revised revised.pdf --compare

As Library

from scripts.main import GrantMockReviewer

reviewer = GrantMockReviewer()
result = reviewer.review(
    proposal_text=proposal_content,
    grant_type="R01",
    section="full"
)
print(result.summary_statement)
print(result.scores)

Parameters

NIH Scoring System

Overall Impact Score (1-9)

The single most important score reflecting the likelihood of the project to exert a sustained, powerful influence on the research field.

Individual Criteria (1-9 each)

1. Significance: Does the project address an important problem? Will scientific knowledge be advanced?
2. Investigator(s): Are the PIs well-suited? Adequate experience and training?
3. Innovation: Does it challenge current paradigms? Novel concepts, approaches, methods?
4. Approach: Sound research design? Appropriate methods? Adequate controls? Address pitfalls?
5. Environment: Adequate institutional support? Scientific environment conducive to success?

Score Interpretation

• 1-3 (High Priority): Compelling, well-developed proposals with strong approach
• 4-5 (Medium Priority): Good proposals with some weaknesses
• 6-9 (Low Priority): Significant weaknesses that diminish enthusiasm

Review Output Format

1. Score Summary

Overall Impact: [Score] - [Descriptor]

Criterion Scores:
- Significance: [Score]
- Investigator(s): [Score]
- Innovation: [Score]
- Approach: [Score]
- Environment: [Score]

2. Strengths

Bullet-point list of major strengths by criterion

3. Weaknesses

Bullet-point list of major weaknesses by criterion

4. Detailed Critique

Paragraph-form critique for each criterion following NIH style

5. Summary Statement

Complete narrative synthesis of the review

6. Revision Recommendations

Prioritized, actionable suggestions for improvement

Common Weaknesses Detected

Significance

• Insufficient justification for the research problem
• Incremental rather than transformative impact
• Unclear connection to human health/disease
• Overstatement of clinical significance without evidence

Investigator

• Lack of relevant expertise for proposed aims
• Insufficient track record in key methodologies
• PI overcommitted (excessive effort on other grants)
• Missing key collaborator expertise

Innovation

• Straightforward extension of published work
• Methods are standard rather than novel
• No challenging of existing paradigms
• Incremental rather than breakthrough potential

Approach

• Aims too ambitious for timeframe
• Insufficient preliminary data
• Inadequate experimental controls
• No discussion of pitfalls and alternatives
• Statistical analysis plan missing or inadequate
• Sample size/power calculations absent

Environment

• Inadequate institutional resources
• Missing core facility access
• Lack of relevant equipment
• Insufficient collaborative environment

Technical Difficulty

High - Requires deep understanding of NIH peer review processes, ability to apply standardized scoring rubrics consistently, and generation of clinically/scientifically accurate critique across diverse research domains.

Review Required: Human verification recommended before deployment in production settings.

References

• references/nih_scoring_rubric.md - Complete NIH scoring guidelines
• references/review_criteria_explained.md - Detailed criterion descriptions
• references/common_weaknesses_catalog.md - Database of typical proposal flaws
• references/summary_statement_templates.md - NIH-style statement templates
• references/score_calibration_guide.md - Score assignment guidelines

Best Practices for Users

1. Provide Complete Proposals: The tool works best with full Research Strategy sections
2. Include Preliminary Data: Approach critique depends on feasibility evidence
3. Review Multiple Times: Use iteratively as you revise
4. Compare Versions: Track improvement between drafts
5. Consider Multiple Perspectives: Supplement with human reviewer feedback

Limitations

1. Cannot access external literature to verify claims
2. May not capture domain-specific methodological nuances
3. Scoring is simulated and may not match actual study section scores
4. Best used as preparatory tool, not replacement for human review

Version

1.0.0 - Initial release with NIH R01/R21/R03 support

Risk Assessment

Security Checklist

• [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] Input file paths validated (no ../ traversal)
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no stack traces exposed)
• [ ] Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:

- Performance optimization - Additional feature support