dvcrn Protocol Deviation Classifier
Clinical trial protocol deviation classification tool, based on GCP and ICH E6 guidelines, automatically determines whether deviations belong to "major deviations" or "minor deviations".
Features
- Automatic Classification: Automatically determines severity based on deviation description
- Risk Assessment: Assesses impact on subject safety, data integrity, and scientific validity
- Regulatory Basis: Classification basis complies with GCP, ICH E6, and FDA/EMA guidelines
- Report Generation: Generates deviation classification reports that meet regulatory requirements
- Chinese Support: Full support for Chinese clinical trial scenarios
Deviation Classification Standards
Major/Critical Deviation
Deviations that may affect trial data integrity, subject safety, or trial scientific validity:
| Category | Examples |
|---|---|
| Informed Consent | Performing research procedures without informed consent, using expired/incorrect informed consent forms |
| Inclusion/Exclusion Criteria | Enrolling subjects who don't meet inclusion criteria, enrolling subjects who meet exclusion criteria |
| Investigational Product | Overdose administration, contraindicated concomitant medication, incorrect route of administration, randomization error |
| Safety | Not performing safety monitoring as required by protocol, missing SAE/SUSAR reports, delayed reporting |
| Blinding | Unblinding by unauthorized personnel, unrecorded emergency unblinding procedures |
| Data Integrity | Falsifying/fabricating data, systematic missing of critical data |
| Prohibited Operations | Violating key operational procedures of trial protocol, not performing key efficacy assessments |
Minor Deviation
Deviations unlikely to affect trial data integrity, subject safety, or trial scientific validity:
| Category | Examples |
|---|---|
| Visit Window | Slightly exceeding visit time window (e.g., within a few days), delay of non-critical visits |
| Sample Collection | Minor timing deviations in non-critical sample collection, slight delays in sample processing |
| Questionnaire Completion | Quality of life questionnaires/diary cards submitted a few days late |
| Data Recording | Delays in non-critical data recording, spelling/formatting errors |
| Procedure Execution | Adjustment of secondary procedure execution order, omission of non-critical assessments (e.g., height measurement) |
| Documentation | Delays in source document signatures, missing secondary documents (e.g., non-critical examination reports) |
Usage
Python API
from scripts.main import DeviationClassifier
# Initialize classifier
classifier = DeviationClassifier()
# Classify single deviation
result = classifier.classify(
description="Subject visit delayed by 2 days",
deviation_type="Visit Window"
)
print(result.classification) # "Minor Deviation"
print(result.confidence) # 0.92
print(result.rationale) # Classification rationale explanation
# Batch classification
deviations = [
{"description": "Blood sample collected without informed consent", "type": "Informed Consent"},
{"description": "Quality of life questionnaire submitted 3 days late", "type": "Data Collection"}
]
batch_results = classifier.classify_batch(deviations)
# Generate report
report = classifier.generate_report(batch_results)
CLI Usage
# Classify single deviation
python scripts/main.py classify --description "Subject visit delayed by 2 days" --type "Visit Window"
# Batch classification from file
python scripts/main.py batch --input deviations.json --output report.json
# Interactive classification
python scripts/main.py interactive
# Assess deviation impact
python scripts/main.py assess \
--description "Subject accidentally took double dose of investigational drug" \
--safety-impact high \
--data-impact medium \
--scientific-impact medium
Input Format
JSON Input File Format:
[
{
"id": "DEV-001",
"description": "Subject visit delayed by 2 days",
"type": "Visit Window",
"occurrence_date": "2024-01-15",
"severity_factors": {
"safety_impact": "none",
"data_impact": "low",
"scientific_impact": "low"
}
},
{
"id": "DEV-002",
"description": "Blood collection performed without informed consent",
"type": "Informed Consent",
"severity_factors": {
"safety_impact": "high",
"data_impact": "high",
"scientific_impact": "high"
}
}
]
Output Format
Classification Result:
{
"id": "DEV-001",
"classification": "Minor Deviation",
"classification_en": "Minor Deviation",
"confidence": 0.92,
"rationale": "Visit time window slightly delayed (2 days), does not affect subject safety, data integrity, or trial scientific validity.",
"risk_factors": {
"safety_risk": "none",
"data_integrity_risk": "low",
"scientific_validity_risk": "none"
},
"regulatory_basis": [
"ICH E6(R2) Section 4.5",
"GCP Section 6.4.4"
],
"recommended_actions": [
"Document in file",
"Track trends"
]
}
Classification Algorithm
Classification based on the following assessment dimensions:
Subject Safety Impact (Safety Impact)
- None: No impact
- Low: Minor impact
- Medium: Moderate impact
- High: Serious impact
Data Integrity Impact (Data Integrity Impact)
- None: No impact
- Low: Minor impact on non-critical data
- Medium: Partial impact on critical data
- High: Serious damage to critical data
Trial Scientific Validity Impact (Scientific Validity Impact)
- None: No impact
- Low: Minor impact on statistical power
- Medium: May affect primary endpoint
- High: Seriously affects trial conclusion
Classification Rules:
- Any dimension is High → Major Deviation
- Safety dimension is Medium and Data/Science either is Medium+ → Major Deviation
- Other cases → Minor Deviation
Regulatory Basis
- ICH E6(R2) Good Clinical Practice Guideline
- ICH E6(R3) Good Clinical Practice Guideline (Draft)
- FDA 21 CFR Part 312 (IND Regulations)
- FDA Guidance for Industry: Oversight of Clinical Investigations
- EMA Reflection Paper on Risk Based Quality Management
- NMPA Good Clinical Practice for Drug Clinical Trials
Dependencies
- Python 3.8+
- No third-party dependencies (pure Python standard library implementation)
Notes
- This tool provides classification recommendations, final determination must be confirmed by clinical quality assurance personnel
- Serious/critical deviations must be reported to sponsor and ethics committee immediately
- It is recommended to regularly review deviation trends and implement CAPA (Corrective and Preventive Actions)
- Classification standards may vary by regulatory agency, trial type, and protocol requirements
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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