--- name: clinical-decision-support description: "Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis." allowed-tools: [Read, Write, Edit, Bash] ---

# Clinical Decision Support Documents

## Description

Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:

1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms

All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.

**Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.

## Capabilities

### Document Types

**Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR) - Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI) - Survival analysis with Kaplan-Meier curves and log-rank tests - Efficacy tables and waterfall plots - Comparative effectiveness analyses - Pharmaceutical cohort reporting (trial subgroups, real-world evidence)

**Treatment Recommendation Reports** - Evidence-based treatment guidelines for specific disease states - Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C) - Quality of evidence assessment (high, moderate, low, very low) - Treatment algorithm flowcharts with TikZ diagrams - Line-of-therapy sequencing based on biomarkers - Decision pathways with clinical and molecular criteria - Pharmaceutical strategy documents - Clinical guideline development for medical societies

### Clinical Features

- **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring - **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests - **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment - **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature - **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment - **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions

## Pharmaceutical and Research Use Cases

This skill is specifically designed for pharmaceutical and clinical research applications:

**Drug Development** - **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses - **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups - **Companion Diagnostic Development**: Linking biomarkers to drug response - **Regulatory Submissions**: IND/NDA documentation with evidence summaries

**Medical Affairs** - **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders - **Medical Strategy Documents**: Competitive landscape and positioning strategies - **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks - **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals

**Clinical Guidelines** - **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies - **Consensus Recommendations**: Multi-stakeholder treatment algorithm development - **Practice Standards**: Biomarker-based treatment selection criteria - **Quality Measures**: Evidence-based performance metrics

**Real-World Evidence** - **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data - **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings - **Outcomes Research**: Long-term survival and safety in clinical practice - **Health Economics**: Cost-effectiveness analyses by biomarker subgroup

## When to Use

Use this skill when you need to:

- **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics - **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies - **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios) - **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions - **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms - **Document biomarker-guided therapy selection** at the population level (not individual patients) - **Synthesize evidence** from multiple trials or real-world data sources - **Create clinical decision algorithms** with flowcharts for treatment sequencing

**Do NOT use this skill for:** - Individual patient treatment plans (use `treatment-plans` skill) - Bedside clinical care documentation (use `treatment-plans` skill) - Simple patient-specific treatment protocols (use `treatment-plans` skill)

## Visual Enhancement with Scientific Schematics

**⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**

This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree) 2. For cohort analyses: include patient flow diagram 3. For treatment recommendations: include decision flowchart

**How to generate figures:** - Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams - Simply describe your desired diagram in natural language - Nano Banana Pro will automatically generate, review, and refine the schematic

**How to generate schematics:** ```bash python scripts/generate_schematic.py "your diagram description" -o figures/output.png ```

The AI will automatically: - Create publication-quality images with proper formatting - Review and refine through multiple iterations - Ensure accessibility (colorblind-friendly, high contrast) - Save outputs in the figures/ directory

**When to add schematics:** - Clinical decision algorithm flowcharts - Treatment pathway diagrams - Biomarker stratification trees - Patient cohort flow diagrams (CONSORT-style) - Survival curve visualizations - Molecular mechanism diagrams - Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

## Document Structure

**CRITICAL REQUIREMENT: All clinical decision support documents MUST begin with a complete executive summary on page 1 that spans the entire first page before any table of contents or detailed sections.**

### Page 1 Executive Summary Structure

The first page of every CDS document should contain ONLY the executive summary with the following components:

**Required Elements (all on page 1):** 1. **Document Title and Type** - Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations") - Subtitle with disease state and focus 2. **Report Information Box** (using colored tcolorbox) - Document type and purpose - Date of analysis/report - Disease state and patient population - Author/institution (if applicable) - Analysis framework or methodology 3. **Key Findings Boxes** (3-5 colored boxes using tcolorbox) - **Primary Results** (blue box): Main efficacy/outcome findings - **Biomarker Insights** (green box): Key molecular subtype findings - **Clinical Implications** (yellow/orange box): Actionable treatment implications - **Statistical Summary** (gray box): Hazard ratios, p-values, key statistics - **Safety Highlights** (red box, if applicable): Critical adverse events or warnings

**Visual Requirements:** - Use `\thispagestyle{empty}` to remove page numbers from page 1 - All content must fit on page 1 (before `\newpage`) - Use colored tcolorbox environments with different colors for visual hierarchy - Boxes should be scannable and highlight most critical information - Use bullet points, not narrative paragraphs - End page 1 with `\newpage` before table of contents or detailed sections

**Example First Page LaTeX Structure:** ```latex \maketitle \thispagestyle{empty}

% Report Information Box \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information] \textbf{Document Type:} Patient Cohort Analysis\\ \textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\ \textbf{Analysis Date:} \today\\ \textbf{Population:} 60 patients, biomarker-stratified by HR status \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #1: Primary Results \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results] \begin{itemize} \item Overall ORR: 72\% (95\% CI: 59-83\%) \item Median PFS: 18.5 months (95\% CI: 14.2-22.8) \item Median OS: 35.2 months (95\% CI: 28.1-NR) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #2: Biomarker Insights \begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings] \begin{itemize} \item HR+/HER2+: ORR 68\%, median PFS 16.2 months \item HR-/HER2+: ORR 78\%, median PFS 22.1 months \item HR status significantly associated with outcomes (p=0.041) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #3: Clinical Implications \begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations] \begin{itemize} \item Strong efficacy observed regardless of HR status (Grade 1A) \item HR-/HER2+ patients showed numerically superior outcomes \item Treatment recommended for all HER2+ MBC patients \end{itemize} \end{tcolorbox}

\newpage \tableofcontents % TOC on page 2 \newpage % Detailed content starts page 3 ```

### Patient Cohort Analysis (Detailed Sections - Page 3+) - **Cohort Characteristics**: Demographics, baseline features, patient selection criteria - **Biomarker Stratification**: Molecular subtypes, genomic alterations, IHC profiles - **Treatment Exposure**: Therapies received, dosing, treatment duration by subgroup - **Outcome Analysis**: Response rates (ORR, DCR), survival data (OS, PFS), DOR - **Statistical Methods**: Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regression - **Subgroup Comparisons**: Biomarker-stratified efficacy, forest plots, statistical significance - **Safety Profile**: Adverse events by subgroup, dose modifications, discontinuations - **Clinical Recommendations**: Treatment implications based on biomarker profiles - **Figures**: Waterfall plots, swimmer plots, survival curves, forest plots - **Tables**: Demographics table, biomarker frequency, outcomes by subgroup

### Treatment Recommendation Reports (Detailed Sections - Page 3+)

**Page 1 Executive Summary

--- name: clinical-decision-support description: "Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis." allowed-tools: [Read, Write, Edit, Bash] ---

# Clinical Decision Support Documents

## Description

Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:

1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms

All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.

**Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.

## Capabilities

### Document Types

**Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR) - Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI) - Survival analysis with Kaplan-Meier curves and log-rank tests - Efficacy tables and waterfall plots - Comparative effectiveness analyses - Pharmaceutical cohort reporting (trial subgroups, real-world evidence)

**Treatment Recommendation Reports** - Evidence-based treatment guidelines for specific disease states - Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C) - Quality of evidence assessment (high, moderate, low, very low) - Treatment algorithm flowcharts with TikZ diagrams - Line-of-therapy sequencing based on biomarkers - Decision pathways with clinical and molecular criteria - Pharmaceutical strategy documents - Clinical guideline development for medical societies

### Clinical Features

- **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring - **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests - **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment - **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature - **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment - **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions

## Pharmaceutical and Research Use Cases

This skill is specifically designed for pharmaceutical and clinical research applications:

**Drug Development** - **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses - **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups - **Companion Diagnostic Development**: Linking biomarkers to drug response - **Regulatory Submissions**: IND/NDA documentation with evidence summaries

**Medical Affairs** - **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders - **Medical Strategy Documents**: Competitive landscape and positioning strategies - **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks - **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals

**Clinical Guidelines** - **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies - **Consensus Recommendations**: Multi-stakeholder treatment algorithm development - **Practice Standards**: Biomarker-based treatment selection criteria - **Quality Measures**: Evidence-based performance metrics

**Real-World Evidence** - **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data - **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings - **Outcomes Research**: Long-term survival and safety in clinical practice - **Health Economics**: Cost-effectiveness analyses by biomarker subgroup

## When to Use

Use this skill when you need to:

- **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics - **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies - **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios) - **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions - **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms - **Document biomarker-guided therapy selection** at the population level (not individual patients) - **Synthesize evidence** from multiple trials or real-world data sources - **Create clinical decision algorithms** with flowcharts for treatment sequencing

**Do NOT use this skill for:** - Individual patient treatment plans (use `treatment-plans` skill) - Bedside clinical care documentation (use `treatment-plans` skill) - Simple patient-specific treatment protocols (use `treatment-plans` skill)

## Visual Enhancement with Scientific Schematics

**⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**

This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree) 2. For cohort analyses: include patient flow diagram 3. For treatment recommendations: include decision flowchart

**How to generate figures:** - Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams - Simply describe your desired diagram in natural language - Nano Banana Pro will automatically generate, review, and refine the schematic

**How to generate schematics:** ```bash python scripts/generate_schematic.py "your diagram description" -o figures/output.png ```

The AI will automatically: - Create publication-quality images with proper formatting - Review and refine through multiple iterations - Ensure accessibility (colorblind-friendly, high contrast) - Save outputs in the figures/ directory

**When to add schematics:** - Clinical decision algorithm flowcharts - Treatment pathway diagrams - Biomarker stratification trees - Patient cohort flow diagrams (CONSORT-style) - Survival curve visualizations - Molecular mechanism diagrams - Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

## Document Structure

**CRITICAL REQUIREMENT: All clinical decision support documents MUST begin with a complete executive summary on page 1 that spans the entire first page before any table of contents or detailed sections.**

### Page 1 Executive Summary Structure

The first page of every CDS document should contain ONLY the executive summary with the following components:

**Required Elements (all on page 1):** 1. **Document Title and Type** - Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations") - Subtitle with disease state and focus 2. **Report Information Box** (using colored tcolorbox) - Document type and purpose - Date of analysis/report - Disease state and patient population - Author/institution (if applicable) - Analysis framework or methodology 3. **Key Findings Boxes** (3-5 colored boxes using tcolorbox) - **Primary Results** (blue box): Main efficacy/outcome findings - **Biomarker Insights** (green box): Key molecular subtype findings - **Clinical Implications** (yellow/orange box): Actionable treatment implications - **Statistical Summary** (gray box): Hazard ratios, p-values, key statistics - **Safety Highlights** (red box, if applicable): Critical adverse events or warnings

**Visual Requirements:** - Use `\thispagestyle{empty}` to remove page numbers from page 1 - All content must fit on page 1 (before `\newpage`) - Use colored tcolorbox environments with different colors for visual hierarchy - Boxes should be scannable and highlight most critical information - Use bullet points, not narrative paragraphs - End page 1 with `\newpage` before table of contents or detailed sections

**Example First Page LaTeX Structure:** ```latex \maketitle \thispagestyle{empty}

% Report Information Box \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information] \textbf{Document Type:} Patient Cohort Analysis\\ \textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\ \textbf{Analysis Date:} \today\\ \textbf{Population:} 60 patients, biomarker-stratified by HR status \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #1: Primary Results \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results] \begin{itemize} \item Overall ORR: 72\% (95\% CI: 59-83\%) \item Median PFS: 18.5 months (95\% CI: 14.2-22.8) \item Median OS: 35.2 months (95\% CI: 28.1-NR) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #2: Biomarker Insights \begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings] \begin{itemize} \item HR+/HER2+: ORR 68\%, median PFS 16.2 months \item HR-/HER2+: ORR 78\%, median PFS 22.1 months \item HR status significantly associated with outcomes (p=0.041) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #3: Clinical Implications \begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations] \begin{itemize} \item Strong efficacy observed regardless of HR status (Grade 1A) \item HR-/HER2+ patients showed numerically superior outcomes \item Treatment recommended for all HER2+ MBC patients \end{itemize} \end{tcolorbox}

\newpage \tableofcontents % TOC on page 2 \newpage % Detailed content starts page 3 ```

### Patient Cohort Analysis (Detailed Sections - Page 3+) - **Cohort Characteristics**: Demographics, baseline features, patient selection criteria - **Biomarker Stratification**: Molecular subtypes, genomic alterations, IHC profiles - **Treatment Exposure**: Therapies received, dosing, treatment duration by subgroup - **Outcome Analysis**: Response rates (ORR, DCR), survival data (OS, PFS), DOR - **Statistical Methods**: Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regression - **Subgroup Comparisons**: Biomarker-stratified efficacy, forest plots, statistical significance - **Safety Profile**: Adverse events by subgroup, dose modifications, discontinuations - **Clinical Recommendations**: Treatment implications based on biomarker profiles - **Figures**: Waterfall plots, swimmer plots, survival curves, forest plots - **Tables**: Demographics table, biomarker frequency, outcomes by subgroup

### Treatment Recommendation Reports (Detailed Sections - Page 3+)

**Page 1 Executive Summary

--- name: clinical-decision-support description: "Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis." allowed-tools: [Read, Write, Edit, Bash] ---

# Clinical Decision Support Documents

## Description

Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:

1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms

All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.

**Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.

## Capabilities

### Document Types

**Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR) - Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI) - Survival analysis with Kaplan-Meier curves and log-rank tests - Efficacy tables and waterfall plots - Comparative effectiveness analyses - Pharmaceutical cohort reporting (trial subgroups, real-world evidence)

**Treatment Recommendation Reports** - Evidence-based treatment guidelines for specific disease states - Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C) - Quality of evidence assessment (high, moderate, low, very low) - Treatment algorithm flowcharts with TikZ diagrams - Line-of-therapy sequencing based on biomarkers - Decision pathways with clinical and molecular criteria - Pharmaceutical strategy documents - Clinical guideline development for medical societies

### Clinical Features

- **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring - **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests - **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment - **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature - **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment - **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions

## Pharmaceutical and Research Use Cases

This skill is specifically designed for pharmaceutical and clinical research applications:

**Drug Development** - **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses - **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups - **Companion Diagnostic Development**: Linking biomarkers to drug response - **Regulatory Submissions**: IND/NDA documentation with evidence summaries

**Medical Affairs** - **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders - **Medical Strategy Documents**: Competitive landscape and positioning strategies - **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks - **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals

**Clinical Guidelines** - **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies - **Consensus Recommendations**: Multi-stakeholder treatment algorithm development - **Practice Standards**: Biomarker-based treatment selection criteria - **Quality Measures**: Evidence-based performance metrics

**Real-World Evidence** - **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data - **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings - **Outcomes Research**: Long-term survival and safety in clinical practice - **Health Economics**: Cost-effectiveness analyses by biomarker subgroup

## When to Use

Use this skill when you need to:

- **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics - **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies - **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios) - **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions - **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms - **Document biomarker-guided therapy selection** at the population level (not individual patients) - **Synthesize evidence** from multiple trials or real-world data sources - **Create clinical decision algorithms** with flowcharts for treatment sequencing

**Do NOT use this skill for:** - Individual patient treatment plans (use `treatment-plans` skill) - Bedside clinical care documentation (use `treatment-plans` skill) - Simple patient-specific treatment protocols (use `treatment-plans` skill)

## Visual Enhancement with Scientific Schematics

**⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**

This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree) 2. For cohort analyses: include patient flow diagram 3. For treatment recommendations: include decision flowchart

**How to generate figures:** - Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams - Simply describe your desired diagram in natural language - Nano Banana Pro will automatically generate, review, and refine the schematic

**How to generate schematics:** ```bash python scripts/generate_schematic.py "your diagram description" -o figures/output.png ```

The AI will automatically: - Create publication-quality images with proper formatting - Review and refine through multiple iterations - Ensure accessibility (colorblind-friendly, high contrast) - Save outputs in the figures/ directory

**When to add schematics:** - Clinical decision algorithm flowcharts - Treatment pathway diagrams - Biomarker stratification trees - Patient cohort flow diagrams (CONSORT-style) - Survival curve visualizations - Molecular mechanism diagrams - Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

## Document Structure

**CRITICAL REQUIREMENT: All clinical decision support documents MUST begin with a complete executive summary on page 1 that spans the entire first page before any table of contents or detailed sections.**

### Page 1 Executive Summary Structure

The first page of every CDS document should contain ONLY the executive summary with the following components:

**Required Elements (all on page 1):** 1. **Document Title and Type** - Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations") - Subtitle with disease state and focus 2. **Report Information Box** (using colored tcolorbox) - Document type and purpose - Date of analysis/report - Disease state and patient population - Author/institution (if applicable) - Analysis framework or methodology 3. **Key Findings Boxes** (3-5 colored boxes using tcolorbox) - **Primary Results** (blue box): Main efficacy/outcome findings - **Biomarker Insights** (green box): Key molecular subtype findings - **Clinical Implications** (yellow/orange box): Actionable treatment implications - **Statistical Summary** (gray box): Hazard ratios, p-values, key statistics - **Safety Highlights** (red box, if applicable): Critical adverse events or warnings

**Visual Requirements:** - Use `\thispagestyle{empty}` to remove page numbers from page 1 - All content must fit on page 1 (before `\newpage`) - Use colored tcolorbox environments with different colors for visual hierarchy - Boxes should be scannable and highlight most critical information - Use bullet points, not narrative paragraphs - End page 1 with `\newpage` before table of contents or detailed sections

**Example First Page LaTeX Structure:** ```latex \maketitle \thispagestyle{empty}

% Report Information Box \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information] \textbf{Document Type:} Patient Cohort Analysis\\ \textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\ \textbf{Analysis Date:} \today\\ \textbf{Population:} 60 patients, biomarker-stratified by HR status \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #1: Primary Results \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results] \begin{itemize} \item Overall ORR: 72\% (95\% CI: 59-83\%) \item Median PFS: 18.5 months (95\% CI: 14.2-22.8) \item Median OS: 35.2 months (95\% CI: 28.1-NR) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #2: Biomarker Insights \begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings] \begin{itemize} \item HR+/HER2+: ORR 68\%, median PFS 16.2 months \item HR-/HER2+: ORR 78\%, median PFS 22.1 months \item HR status significantly associated with outcomes (p=0.041) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #3: Clinical Implications \begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations] \begin{itemize} \item Strong efficacy observed regardless of HR status (Grade 1A) \item HR-/HER2+ patients showed numerically superior outcomes \item Treatment recommended for all HER2+ MBC patients \end{itemize} \end{tcolorbox}

\newpage \tableofcontents % TOC on page 2 \newpage % Detailed content starts page 3 ```

### Patient Cohort Analysis (Detailed Sections - Page 3+) - **Cohort Characteristics**: Demographics, baseline features, patient selection criteria - **Biomarker Stratification**: Molecular subtypes, genomic alterations, IHC profiles - **Treatment Exposure**: Therapies received, dosing, treatment duration by subgroup - **Outcome Analysis**: Response rates (ORR, DCR), survival data (OS, PFS), DOR - **Statistical Methods**: Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regression - **Subgroup Comparisons**: Biomarker-stratified efficacy, forest plots, statistical significance - **Safety Profile**: Adverse events by subgroup, dose modifications, discontinuations - **Clinical Recommendations**: Treatment implications based on biomarker profiles - **Figures**: Waterfall plots, swimmer plots, survival curves, forest plots - **Tables**: Demographics table, biomarker frequency, outcomes by subgroup

### Treatment Recommendation Reports (Detailed Sections - Page 3+)

**Page 1 Executive Summary

--- name: clinical-decision-support description: "Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis." allowed-tools: [Read, Write, Edit, Bash] ---

# Clinical Decision Support Documents

## Description

Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:

1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms

All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.

**Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.

## Capabilities

### Document Types

**Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR) - Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI) - Survival analysis with Kaplan-Meier curves and log-rank tests - Efficacy tables and waterfall plots - Comparative effectiveness analyses - Pharmaceutical cohort reporting (trial subgroups, real-world evidence)

**Treatment Recommendation Reports** - Evidence-based treatment guidelines for specific disease states - Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C) - Quality of evidence assessment (high, moderate, low, very low) - Treatment algorithm flowcharts with TikZ diagrams - Line-of-therapy sequencing based on biomarkers - Decision pathways with clinical and molecular criteria - Pharmaceutical strategy documents - Clinical guideline development for medical societies

### Clinical Features

- **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring - **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests - **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment - **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature - **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment - **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions

## Pharmaceutical and Research Use Cases

This skill is specifically designed for pharmaceutical and clinical research applications:

**Drug Development** - **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses - **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups - **Companion Diagnostic Development**: Linking biomarkers to drug response - **Regulatory Submissions**: IND/NDA documentation with evidence summaries

**Medical Affairs** - **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders - **Medical Strategy Documents**: Competitive landscape and positioning strategies - **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks - **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals

**Clinical Guidelines** - **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies - **Consensus Recommendations**: Multi-stakeholder treatment algorithm development - **Practice Standards**: Biomarker-based treatment selection criteria - **Quality Measures**: Evidence-based performance metrics

**Real-World Evidence** - **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data - **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings - **Outcomes Research**: Long-term survival and safety in clinical practice - **Health Economics**: Cost-effectiveness analyses by biomarker subgroup

## When to Use

Use this skill when you need to:

- **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics - **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies - **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios) - **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions - **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms - **Document biomarker-guided therapy selection** at the population level (not individual patients) - **Synthesize evidence** from multiple trials or real-world data sources - **Create clinical decision algorithms** with flowcharts for treatment sequencing

**Do NOT use this skill for:** - Individual patient treatment plans (use `treatment-plans` skill) - Bedside clinical care documentation (use `treatment-plans` skill) - Simple patient-specific treatment protocols (use `treatment-plans` skill)

## Visual Enhancement with Scientific Schematics

**⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**

This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree) 2. For cohort analyses: include patient flow diagram 3. For treatment recommendations: include decision flowchart

**How to generate figures:** - Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams - Simply describe your desired diagram in natural language - Nano Banana Pro will automatically generate, review, and refine the schematic

**How to generate schematics:** ```bash python scripts/generate_schematic.py "your diagram description" -o figures/output.png ```

The AI will automatically: - Create publication-quality images with proper formatting - Review and refine through multiple iterations - Ensure accessibility (colorblind-friendly, high contrast) - Save outputs in the figures/ directory

**When to add schematics:** - Clinical decision algorithm flowcharts - Treatment pathway diagrams - Biomarker stratification trees - Patient cohort flow diagrams (CONSORT-style) - Survival curve visualizations - Molecular mechanism diagrams - Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

## Document Structure

**CRITICAL REQUIREMENT: All clinical decision support documents MUST begin with a complete executive summary on page 1 that spans the entire first page before any table of contents or detailed sections.**

### Page 1 Executive Summary Structure

The first page of every CDS document should contain ONLY the executive summary with the following components:

**Required Elements (all on page 1):** 1. **Document Title and Type** - Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations") - Subtitle with disease state and focus 2. **Report Information Box** (using colored tcolorbox) - Document type and purpose - Date of analysis/report - Disease state and patient population - Author/institution (if applicable) - Analysis framework or methodology 3. **Key Findings Boxes** (3-5 colored boxes using tcolorbox) - **Primary Results** (blue box): Main efficacy/outcome findings - **Biomarker Insights** (green box): Key molecular subtype findings - **Clinical Implications** (yellow/orange box): Actionable treatment implications - **Statistical Summary** (gray box): Hazard ratios, p-values, key statistics - **Safety Highlights** (red box, if applicable): Critical adverse events or warnings

**Visual Requirements:** - Use `\thispagestyle{empty}` to remove page numbers from page 1 - All content must fit on page 1 (before `\newpage`) - Use colored tcolorbox environments with different colors for visual hierarchy - Boxes should be scannable and highlight most critical information - Use bullet points, not narrative paragraphs - End page 1 with `\newpage` before table of contents or detailed sections

**Example First Page LaTeX Structure:** ```latex \maketitle \thispagestyle{empty}

% Report Information Box \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information] \textbf{Document Type:} Patient Cohort Analysis\\ \textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\ \textbf{Analysis Date:} \today\\ \textbf{Population:} 60 patients, biomarker-stratified by HR status \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #1: Primary Results \begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results] \begin{itemize} \item Overall ORR: 72\% (95\% CI: 59-83\%) \item Median PFS: 18.5 months (95\% CI: 14.2-22.8) \item Median OS: 35.2 months (95\% CI: 28.1-NR) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #2: Biomarker Insights \begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings] \begin{itemize} \item HR+/HER2+: ORR 68\%, median PFS 16.2 months \item HR-/HER2+: ORR 78\%, median PFS 22.1 months \item HR status significantly associated with outcomes (p=0.041) \end{itemize} \end{tcolorbox}

\vspace{0.3cm}

% Key Finding #3: Clinical Implications \begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations] \begin{itemize} \item Strong efficacy observed regardless of HR status (Grade 1A) \item HR-/HER2+ patients showed numerically superior outcomes \item Treatment recommended for all HER2+ MBC patients \end{itemize} \end{tcolorbox}

\newpage \tableofcontents % TOC on page 2 \newpage % Detailed content starts page 3 ```

### Patient Cohort Analysis (Detailed Sections - Page 3+) - **Cohort Characteristics**: Demographics, baseline features, patient selection criteria - **Biomarker Stratification**: Molecular subtypes, genomic alterations, IHC profiles - **Treatment Exposure**: Therapies received, dosing, treatment duration by subgroup - **Outcome Analysis**: Response rates (ORR, DCR), survival data (OS, PFS), DOR - **Statistical Methods**: Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regression - **Subgroup Comparisons**: Biomarker-stratified efficacy, forest plots, statistical significance - **Safety Profile**: Adverse events by subgroup, dose modifications, discontinuations - **Clinical Recommendations**: Treatment implications based on biomarker profiles - **Figures**: Waterfall plots, swimmer plots, survival curves, forest plots - **Tables**: Demographics table, biomarker frequency, outcomes by subgroup

### Treatment Recommendation Reports (Detailed Sections - Page 3+)

**Page 1 Executive Summary