Protocol Deviation Classifier

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".

When to Use

Key Features

See ## Features above for related details.

Dependencies

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/protocol-deviation-classifier"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan: 1. Confirm the user input, output path, and any required config values. 2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings. 3. Run python scripts/main.py with the validated inputs. 4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

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 --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json

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.

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

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

Output Requirements

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

Error Handling

Input Validation

This skill accepts requests that match the documented purpose of protocol-deviation-classifier and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

> protocol-deviation-classifier only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits 7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

Inputs to Collect

Output Contract

Validation and Safety Rules

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