inclusion-criteria-gen（纳入标准生成）

Inclusion Criteria Generator

Generate and optimize clinical trial subject inclusion/exclusion criteria to balance scientific rigor with recruitment feasibility.

Use Cases

• Protocol Design: Create initial eligibility criteria for new clinical trials
• Criteria Optimization: Refine existing criteria to improve enrollment without compromising safety/efficacy
• Competitive Analysis: Analyze eligibility patterns across similar trials
• Recruitment Strategy: Identify and mitigate barriers to enrollment
• Feasibility Assessment: Evaluate if proposed criteria are realistic for target population

Usage

CLI Usage

# Generate criteria from study design
python scripts/main.py generate \
  --indication "Type 2 Diabetes" \
  --phase "Phase 2" \
  --population "adults" \
  --duration "24 weeks" \
  --output criteria.json

# Optimize existing criteria
python scripts/main.py optimize \
  --input current_criteria.json \
  --enrollment-target 200 \
  --current-enrollment 120 \
  --output optimized_criteria.json

# Analyze criteria complexity
python scripts/main.py analyze \
  --input criteria.json \
  --output analysis_report.json

# Compare with competitor trials
python scripts/main.py benchmark \
  --input criteria.json \
  --condition "Type 2 Diabetes" \
  --output benchmark_report.json

Python API

from scripts.main import CriteriaGenerator, CriteriaOptimizer

# Generate new criteria
generator = CriteriaGenerator()
criteria = generator.generate(
    indication="Type 2 Diabetes",
    phase="Phase 2",
    population="adults",
    study_duration="24 weeks",
    endpoints=["HbA1c reduction", "weight change"]
)

# Optimize existing criteria
optimizer = CriteriaOptimizer()
optimized = optimizer.optimize(
    criteria=existing_criteria,
    enrollment_target=200,
    current_enrollment=120,
    retention_rate=0.85
)

# Analyze criteria complexity
analysis = optimizer.analyze_complexity(criteria)

Input Format

Study Design Parameters

{
  "indication": "Type 2 Diabetes Mellitus",
  "phase": "Phase 2",
  "population": "adults",
  "age_range": {"min": 18, "max": 75},
  "study_duration": "24 weeks",
  "treatment_type": "oral",
  "primary_endpoints": ["HbA1c change from baseline"],
  "safety_considerations": ["cardiovascular risk"],
  "concomitant_meds_allowed": ["metformin"]
}

Existing Criteria Format

{
  "inclusion_criteria": [
    {
      "id": "I1",
      "criterion": "Age 18-75 years",
      "rationale": "Adult population per regulatory guidance",
      "category": "demographics"
    }
  ],
  "exclusion_criteria": [
    {
      "id": "E1",
      "criterion": "HbA1c < 7.0% or > 11.0%",
      "rationale": "Ensure measurable treatment effect",
      "category": "disease_severity"
    }
  ]
}

Output Format

Generated/Optimized Criteria

{
  "inclusion_criteria": [
    {
      "id": "I1",
      "criterion": "Age 18-75 years, inclusive",
      "category": "demographics",
      "rationale": "Adult population; upper limit for safety",
      "priority": "required",
      "impact": "low"
    }
  ],
  "exclusion_criteria": [
    {
      "id": "E1",
      "criterion": "HbA1c < 7.5% or > 10.5% at screening",
      "category": "disease_severity",
      "rationale": "Optimal range for detecting treatment effect",
      "priority": "required",
      "impact": "medium",
      "flexibility": "widen by 0.5% if enrollment slow"
    }
  ],
  "optimization_notes": [
    "Widened HbA1c range from 7.0-11.0% to 7.5-10.5% based on feasibility data"
  ],
  "recruitment_metrics": {
    "estimated_screen_success_rate": 0.35,
    "estimated_enrollment_rate": 0.65,
    "key_barriers": ["HbA1c upper limit", "concomitant medication restrictions"]
  }
}

Criteria Categories

Optimization Strategies

Common Modifications

Retention Considerations

• Minimize visit frequency when possible
• Allow window periods for visit timing
• Provide transportation assistance language
• Consider patient-reported outcome burden

Technical Details

• Difficulty: Medium
• Standards: ICH E6(R2) GCP, CDISC Protocol Representation Model
• Data Sources: ClinicalTrials.gov eligibility patterns, literature feasibility data
• Dependencies: None (pure Python)

References

• references/criteria_templates.json - Templates by therapeutic area
• references/optimization_guidelines.md - Best practices for criteria optimization
• references/common_pitfalls.md - Frequent eligibility design mistakes
• references/regulatory_guidance.md - FDA/EMA guidance on eligibility criteria
• references/feasibility_data.json - Screen failure rates by criterion type

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
• [ ] API requests use HTTPS only
• [ ] Input validated against allowed patterns
• [ ] API timeout and retry mechanisms implemented
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no internal paths exposed)
• [ ] Dependencies audited
• [ ] No exposure of internal service architecture

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

Parameters