---

name: protocol-deviation-classifier description: 'Determine whether an incident in a clinical trial is a "major deviation" or "minor deviation".

Function: Automatically classify protocol deviations in clinical trials based on GCP/ICH E6 standards,

assessing the impact on subject safety, data integrity, and trial scientific validity.

Trigger: When classification assessment of protocol deviations is needed, input deviation event description or deviation type.

Use cases: Clinical trial quality management, deviation impact assessment, regulatory submission preparation, audit preparation.

' version: 1.0.0 category: Pharma tags:

• clinical-trials
• gcp
• compliance
• deviation
• quality
• ich-e6
• ctm author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

---

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:

1. Subject Safety Impact (Safety Impact)

   • None: No impact
   • Low: Minor impact
   • Medium: Moderate impact
   • High: Serious impact

2. 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

3. 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

1. This tool provides classification recommendations, final determination must be confirmed by clinical quality assurance personnel
2. Serious/critical deviations must be reported to sponsor and ethics committee immediately
3. It is recommended to regularly review deviation trends and implement CAPA (Corrective and Preventive Actions)
4. 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

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