Protocol Deviation Classifier

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:

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

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:

Regulatory Basis

Dependencies

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

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

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"
  ]
}

# Python dependencies
pip install -r requirements.txt

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