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

CODEBLOCK0

CLI Usage

CODEBLOCK1

Input Format

JSON Input File Format:

CODEBLOCK2

Output Format

Classification Result:

CODEBLOCK3

Classification Algorithm

Classification based on the following assessment dimensions:

1. 1. Subject Safety Impact (Safety Impact)

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

1. 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

1. 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. 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

CODEBLOCK4

Evaluation Criteria

Success Metrics

• - [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. 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