130 lines
4.9 KiB
Markdown
130 lines
4.9 KiB
Markdown
# Clinical Decision Support Skill
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Professional clinical decision support documents for medical professionals in pharmaceutical and clinical research settings.
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## Quick Start
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This skill enables generation of three types of clinical documents:
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1. **Individual Patient Treatment Plans** - Personalized protocols for specific patients
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2. **Patient Cohort Analysis** - Biomarker-stratified group analyses with outcomes
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3. **Treatment Recommendation Reports** - Evidence-based clinical guidelines
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All documents are generated as compact, professional LaTeX/PDF files.
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## Directory Structure
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```
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clinical-decision-support/
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├── SKILL.md # Main skill definition
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├── README.md # This file
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│
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├── references/ # Clinical guidance documents
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│ ├── patient_cohort_analysis.md
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│ ├── treatment_recommendations.md
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│ ├── clinical_decision_algorithms.md
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│ ├── biomarker_classification.md
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│ ├── outcome_analysis.md
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│ └── evidence_synthesis.md
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│
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├── assets/ # Templates and examples
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│ ├── cohort_analysis_template.tex
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│ ├── treatment_recommendation_template.tex
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│ ├── clinical_pathway_template.tex
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│ ├── biomarker_report_template.tex
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│ ├── example_gbm_cohort.md
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│ ├── recommendation_strength_guide.md
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│ └── color_schemes.tex
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│
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└── scripts/ # Analysis and generation tools
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├── generate_survival_analysis.py
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├── create_cohort_tables.py
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├── build_decision_tree.py
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├── biomarker_classifier.py
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└── validate_cds_document.py
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```
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## Example Use Cases
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### Create a Patient Cohort Analysis
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```
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> Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression
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(<1%, 1-49%, ≥50%) including ORR, PFS, and OS outcomes
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```
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### Generate Treatment Recommendations
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```
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> Create evidence-based treatment recommendations for HER2-positive
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metastatic breast cancer with GRADE methodology
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```
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### Build Clinical Pathway
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```
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> Generate a clinical decision algorithm for acute chest pain
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management with TIMI risk score
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```
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## Key Features
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- **GRADE Methodology**: Evidence quality grading (High/Moderate/Low/Very Low)
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- **Recommendation Strength**: Strong (Grade 1) vs Conditional (Grade 2)
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- **Biomarker Integration**: Genomic, expression, and molecular subtype classification
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- **Statistical Analysis**: Kaplan-Meier, Cox regression, log-rank tests
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- **Guideline Concordance**: NCCN, ASCO, ESMO, AHA/ACC integration
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- **Professional Output**: 0.5in margins, color-coded boxes, publication-ready
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## Dependencies
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Python scripts require:
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- `pandas`, `numpy`, `scipy`: Data analysis and statistics
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- `lifelines`: Survival analysis (Kaplan-Meier, Cox regression)
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- `matplotlib`: Visualization
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- `pyyaml` (optional): YAML input for decision trees
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Install with:
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```bash
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pip install pandas numpy scipy lifelines matplotlib pyyaml
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```
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## References Included
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1. **Patient Cohort Analysis**: Stratification methods, biomarker correlations, statistical comparisons
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2. **Treatment Recommendations**: Evidence grading, treatment sequencing, special populations
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3. **Clinical Decision Algorithms**: Risk scores, decision trees, TikZ flowcharts
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4. **Biomarker Classification**: Genomic alterations, molecular subtypes, companion diagnostics
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5. **Outcome Analysis**: Survival methods, response criteria (RECIST), effect sizes
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6. **Evidence Synthesis**: Guideline integration, systematic reviews, meta-analysis
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## Templates Provided
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1. **Cohort Analysis**: Demographics table, biomarker profile, outcomes, statistics, recommendations
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2. **Treatment Recommendations**: Evidence review, GRADE-graded options, monitoring, decision algorithm
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3. **Clinical Pathway**: TikZ flowchart with risk stratification and urgency-coded actions
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4. **Biomarker Report**: Genomic profiling with tier-based actionability and therapy matching
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## Scripts Included
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1. **`generate_survival_analysis.py`**: Create Kaplan-Meier curves with hazard ratios
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2. **`create_cohort_tables.py`**: Generate baseline, efficacy, and safety tables
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3. **`build_decision_tree.py`**: Convert text/JSON to TikZ flowcharts
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4. **`biomarker_classifier.py`**: Stratify patients by PD-L1, HER2, molecular subtypes
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5. **`validate_cds_document.py`**: Quality checks for completeness and compliance
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## Integration
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Integrates with existing skills:
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- **scientific-writing**: Citation management, statistical reporting
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- **clinical-reports**: Medical terminology, HIPAA compliance
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- **scientific-schematics**: TikZ flowcharts
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## Version
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Version 1.0 - Initial release
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Created: November 2024
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Last Updated: November 5, 2024
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## Questions or Feedback
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This skill was designed for pharmaceutical and clinical research professionals creating clinical decision support documents. For questions about usage or suggestions for improvements, contact the Scientific Writer development team.
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