Inclusion Criteria Gen

Source: https://github.com/aipoch/medical-research-skills

Inclusion Criteria Generator

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

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py generate --help

When to Use

• Use this skill when the task is to Generate and optimize clinical trial subject inclusion/exclusion criteria to balance scientific rigor with recruitment feasibility.
• Use this skill for protocol design tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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

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

Parameters

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.