Translational Gap Analyzer

Source: https://github.com/aipoch/medical-research-skills

Translational Gap Analyzer

ID: 209

When to Use

• Use this skill when the task needs Assess translational gaps between preclinical models and human diseases.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Assess translational gaps between preclinical models and human diseases.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python 3.8+
• Built-in libraries: argparse, json, sys

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Evidence Insight/translational-gap-analyzer"
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.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

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

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.

Description

Assesses the "translational gap" between basic research models (such as mice, zebrafish, cell lines) and human diseases, providing early warning of clinical translation failure risks. This system helps researchers identify potential translational barriers in preclinical research and improve clinical trial success rates through multi-dimensional analysis.

Capabilities

• Evaluates anatomical/physiological differences between models and humans
• Analyzes pathological similarity of disease models
• Identifies interspecies differences in molecular pathways
• Evaluates pharmacokinetic differences
• Provides early warning of clinical trial failure risk factors
• Provides improvement recommendations to increase translation success rates

Usage

# Full assessment report
python scripts/main.py --model <model_type> --disease <disease_name> --full

# Quick risk assessment
python scripts/main.py --model <model_type> --disease <disease_name> --quick

# Compare multiple models
python scripts/main.py --models mouse,rat,primate --disease <disease_name> --compare

# Specify focus areas
python scripts/main.py --model mouse --disease "Alzheimer's" --focus metabolism,immune

Arguments

Example Output

{
  "model": "mouse",
  "disease": "Alzheimer's Disease",
  "overall_gap_score": 6.8,
  "risk_level": "HIGH",
  "dimensions": {
    "genetics": {"score": 8.5, "concerns": ["APOE4 differences", "Different tau pathology patterns"]},
    "physiology": {"score": 7.0, "concerns": ["Brain structure differences", "Lifespan differences"]},
    "metabolism": {"score": 6.5, "concerns": ["Significant drug metabolism differences"]},
    "immune": {"score": 5.5, "concerns": ["Microglia functional differences", "Different neuroinflammation patterns"]},
    "behavior": {"score": 6.0, "concerns": ["Limitations in cognitive assessment methods"]}
  },
  "clinical_failure_predictors": [
    "Immune-related mechanism research may not translate",
    "Drug clearance rate differences may lead to inappropriate dosing"
  ],
  "recommendations": [
    "Consider using humanized mouse models",
    "Add non-human primate validation experiments",
    "Focus on peripheral immune and central immune interactions"
  ]
}

Model Types

Common Models

Gap Scoring System

• 0-3: Low gap, good translation prospects
• 4-6: Moderate gap, requires additional validation
• 7-8: High gap, significant translation risks exist
• 9-10: Extremely high gap, low translation likelihood