git:20260721.831d49e to v2.0

- 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

- license: MIT License

- required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]

- metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}

- # 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.

- **Writing Style:** For publication-ready documents targeting medical journals, consult the **venue-templates** skill's `medical_journal_styles.md` for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance.

- ## 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:**

- 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 for Treatment Recommendations should include:**

- 1. **Report Information Box**: Disease state, guideline version/date, target population

- 2. **Key Recommendations Box** (green): Top 3-5 GRADE-graded recommendations by line of therapy

- 3. **Biomarker Decision Criteria Box** (blue): Key molecular markers influencing treatment selection

- 4. **Evidence Summary Box** (gray): Major trials supporting recommendations (e.g., KEYNOTE-189, FLAURA)

- 5. **Critical Monitoring Box** (orange/red): Essential safety monitoring requirements

- **Detailed Sections (Page 3+):**

- - **Clinical Context**: Disease state, epidemiology, current treatment landscape

- - **Target Population**: Patient characteristics, biomarker criteria, staging

- - **Evidence Review**: Systematic literature synthesis, guideline summary, trial data

- - **Treatment Options**: Available therapies with mechanism of action

- - **Evidence Grading**: GRADE assessment for each recommendation (1A, 1B, 2A, 2B, 2C)

- - **Recommendations by Line**: First-line, second-line, subsequent therapies

- - **Biomarker-Guided Selection**: Decision criteria based on molecular profiles

- - **Treatment Algorithms**: TikZ flowcharts showing decision pathways

- - **Monitoring Protocol**: Safety assessments, efficacy monitoring, dose modifications

- - **Special Populations**: Elderly, renal/hepatic impairment, comorbidities

- - **References**: Full bibliography with trial names and citations

- ## Output Format

- **MANDATORY FIRST PAGE REQUIREMENT:**

- - **Page 1**: Full-page executive summary with 3-5 colored tcolorbox elements

- - **Page 2**: Table of contents (optional)

- - **Page 3+**: Detailed sections with methods, results, figures, tables

- **Document Specifications:**

- - **Primary**: LaTeX/PDF with 0.5in margins for compact, data-dense presentation

- - **Length**: Typically 5-15 pages (1 page executive summary + 4-14 pages detailed content)

- - **Style**: Publication-ready, pharmaceutical-grade, suitable for regulatory submissions

- - **First Page**: Always a complete executive summary spanning entire page 1 (see Document Structure section)

- **Visual Elements:**

- - **Colors**:

- - Page 1 boxes: blue=data/information, green=biomarkers/recommendations, yellow/orange=clinical implications, red=warnings

- - Recommendation boxes (green=strong recommendation, yellow=conditional, blue=research needed)

- - Biomarker stratification (color-coded molecular subtypes)

- - Statistical significance (color-coded p-values, hazard ratios)

- - **Tables**:

- - Demographics with baseline characteristics

- - Biomarker frequency by subgroup

- - Outcomes table (ORR, PFS, OS, DOR by molecular subtype)

- - Adverse events by cohort

- - Evidence summary tables with GRADE ratings

- - **Figures**:

- - Kaplan-Meier survival curves with log-rank p-values and number at risk tables

- - Waterfall plots showing best response by patient

- - Forest plots for subgroup analyses with confidence intervals

- - TikZ decision algorithm flowcharts

- - Swimmer plots for individual patient timelines

- - **Statistics**: Hazard ratios with 95% CI, p-values, median survival times, landmark survival rates

- - **Compliance**: De-identification per HIPAA Safe Harbor, confidentiality notices for proprietary data

- ## Integration

- This skill integrates with:

- - **scientific-writing**: Citation management, statistical reporting, evidence synthesis

- - **clinical-reports**: Medical terminology, HIPAA compliance, regulatory documentation

- - **scientific-schematics**: TikZ flowcharts for decision algorithms and treatment pathways

- - **treatment-plans**: Individual patient applications of cohort-derived insights (bidirectional)

- ## Key Differentiators from Treatment-Plans Skill

- **Clinical Decision Support (this skill):**

- - **Audience**: Pharmaceutical companies, clinical researchers, guideline committees, medical affairs

- - **Scope**: Population-level analyses, evidence synthesis, guideline development

- - **Focus**: Biomarker stratification, statistical comparisons, evidence grading

- - **Output**: Multi-page analytical documents (5-15 pages typical) with extensive figures and tables

- - **Use Cases**: Drug development, regulatory submissions, clinical practice guidelines, medical strategy

- - **Example**: "Analyze 60 HER2+ breast cancer patients by hormone receptor status with survival outcomes"

- **Treatment-Plans Skill:**

- - **Audience**: Clinicians, patients, care teams

- - **Scope**: Individual patient care planning

- - **Focus**: SMART goals, patient-specific interventions, monitoring plans

- - **Output**: Concise 1-4 page actionable care plans

- - **Use Cases**: Bedside clinical care, EMR documentation, patient-centered planning

- - **Example**: "Create treatment plan for a 55-year-old patient with newly diagnosed type 2 diabetes"

- **When to use each:**

- - Use **clinical-decision-support** for: cohort analyses, biomarker stratification studies, treatment guideline development, pharmaceutical strategy documents

- - Use **treatment-plans** for: individual patient care plans, treatment protocols for specific patients, bedside clinical documentation

- ## Example Usage

- ### Patient Cohort Analysis

- **Example 1: NSCLC Biomarker Stratification**

- ```

- > Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression (<1%, 1-49%, ≥50%)

- > receiving pembrolizumab. Include outcomes: ORR, median PFS, median OS with hazard ratios

- > comparing PD-L1 ≥50% vs <50%. Generate Kaplan-Meier curves and waterfall plot.

- ```

- **Example 2: GBM Molecular Subtype Analysis**

- ```

- > Generate cohort analysis for 30 GBM patients classified into Cluster 1 (Mesenchymal-Immune-Active)

- > and Cluster 2 (Proneural) molecular subtypes. Compare outcomes including median OS, 6-month PFS rate,

- > and response to TMZ+bevacizumab. Include biomarker profile table and statistical comparison.

- ```

- **Example 3: Breast Cancer HER2 Cohort**

- ```

- > Analyze 60 HER2-positive metastatic breast cancer patients treated with trastuzumab-deruxtecan,

- > stratified by prior trastuzumab exposure (yes/no). Include ORR, DOR, median PFS with forest plot

- > showing subgroup analyses by hormone receptor status, brain metastases, and number of prior lines.

- ```

- ### Treatment Recommendation Report

- **Example 1: HER2+ Metastatic Breast Cancer Guidelines**

- ```

- > Create evidence-based treatment recommendations for HER2-positive metastatic breast cancer including

- > biomarker-guided therapy selection. Use GRADE system to grade recommendations for first-line

- > (trastuzumab+pertuzumab+taxane), second-line (trastuzumab-deruxtecan), and third-line options.

- > Include decision algorithm flowchart based on brain metastases, hormone receptor status, and prior therapies.

- **Example 2: Advanced NSCLC Treatment Algorithm**

- ```

- > Generate treatment recommendation report for advanced NSCLC based on PD-L1 expression, EGFR mutation,

- > ALK rearrangement, and performance status. Include GRADE-graded recommendations for each molecular subtype,

- > TikZ flowchart for biomarker-directed therapy selection, and evidence tables from KEYNOTE-189, FLAURA,

- > and CheckMate-227 trials.

- ```

- **Example 3: Multiple Myeloma Line-of-Therapy Sequencing**

- ```

- > Create treatment algorithm for newly diagnosed multiple myeloma through relapsed/refractory setting.

- > Include GRADE recommendations for transplant-eligible vs ineligible, high-risk cytogenetics considerations,

- > and sequencing of daratumumab, carfilzomib, and CAR-T therapy. Provide flowchart showing decision points

- > at each line of therapy.

- ## Key Features

-

- ### Biomarker Classification

- - Genomic: Mutations, CNV, gene fusions

- - Expression: RNA-seq, IHC scores

- - Molecular subtypes: Disease-specific classifications

- - Clinical actionability: Therapy selection guidance

-

- ### Outcome Metrics

- - Survival: OS (overall survival), PFS (progression-free survival)

- - Response: ORR (objective response rate), DOR (duration of response), DCR (disease control rate)

- - Quality: ECOG performance status, symptom burden

- - Safety: Adverse events, dose modifications

-

- ### Statistical Methods

- - Survival analysis: Kaplan-Meier curves, log-rank tests

- - Group comparisons: t-tests, chi-square, Fisher's exact

- - Effect sizes: Hazard ratios, odds ratios with 95% CI

- - Significance: p-values, multiple testing corrections

-

- ### Evidence Grading

-

- **GRADE System**

- - **1A**: Strong recommendation, high-quality evidence

- - **1B**: Strong recommendation, moderate-quality evidence

- - **2A**: Weak recommendation, high-quality evidence

- - **2B**: Weak recommendation, moderate-quality evidence

- - **2C**: Weak recommendation, low-quality evidence

-

- **Recommendation Strength**

- - **Strong**: Benefits clearly outweigh risks

- - **Conditional**: Trade-offs exist, patient values important

- - **Research**: Insufficient evidence, clinical trials needed

-

- ## Best Practices

-

- ### For Cohort Analyses

-

- 1. **Patient Selection Transparency**: Clearly document inclusion/exclusion criteria, patient flow, and reasons for exclusions

- 2. **Biomarker Clarity**: Specify assay methods, platforms (e.g., FoundationOne, Caris), cut-points, and validation status

- 3. **Statistical Rigor**:

- - Report hazard ratios with 95% confidence intervals, not just p-values

- - Include median follow-up time for survival analyses

- - Specify statistical tests used (log-rank, Cox regression, Fisher's exact)

- - Account for multiple comparisons when appropriate

- 4. **Outcome Definitions**: Use standard criteria:

- - Response: RECIST 1.1, iRECIST for immunotherapy

- - Adverse events: CTCAE version 5.0

- - Performance status: ECOG or Karnofsky

- 5. **Survival Data Presentation**:

- - Median OS/PFS with 95% CI

- - Landmark survival rates (6-month, 12-month, 24-month)

- - Number at risk tables below Kaplan-Meier curves

- - Censoring clearly indicated

- 6. **Subgroup Analyses**: Pre-specify subgroups; clearly label exploratory vs pre-planned analyses

- 7. **Data Completeness**: Report missing data and how it was handled

-

- ### For Treatment Recommendation Reports

-

- 1. **Evidence Grading Transparency**:

- - Use GRADE system consistently (1A, 1B, 2A, 2B, 2C)

- - Document rationale for each grade

- - Clearly state quality of evidence (high, moderate, low, very low)

- 2. **Comprehensive Evidence Review**:

- - Include phase 3 randomized trials as primary evidence

- - Supplement with phase 2 data for emerging therapies

- - Note real-world evidence and meta-analyses

- - Cite trial names (e.g., KEYNOTE-189, CheckMate-227)

- 3. **Biomarker-Guided Recommendations**:

- - Link specific biomarkers to therapy recommendations

- - Specify testing methods and validated assays

- - Include FDA/EMA approval status for companion diagnostics

- 4. **Clinical Actionability**: Every recommendation should have clear implementation guidance

- 5. **Decision Algorithm Clarity**: TikZ flowcharts should be unambiguous with clear yes/no decision points

- 6. **Special Populations**: Address elderly, renal/hepatic impairment, pregnancy, drug interactions

- 7. **Monitoring Guidance**: Specify safety labs, imaging, and frequency

- 8. **Update Frequency**: Date recommendations and plan for periodic updates

-

- ### General Best Practices

-

- 1. **First Page Executive Summary (MANDATORY)**:

- - ALWAYS create a complete executive summary on page 1 that spans the entire first page

- - Use 3-5 colored tcolorbox elements to highlight key findings

- - No table of contents or detailed sections on page 1

- - Use `\thispagestyle{empty}` and end with `\newpage`

- - This is the single most important page - it should be scannable in 60 seconds

- 2. **De-identification**: Remove all 18 HIPAA identifiers before document generation (Safe Harbor method)

- 3. **Regulatory Compliance**: Include confidentiality notices for proprietary pharmaceutical data

- 4. **Publication-Ready Formatting**: Use 0.5in margins, professional fonts, color-coded sections

- 5. **Reproducibility**: Document all statistical methods to enable replication

- 6. **Conflict of Interest**: Disclose pharmaceutical funding or relationships when applicable

- 7. **Visual Hierarchy**: Use colored boxes consistently (blue=data, green=biomarkers, yellow/orange=recommendations, red=warnings)

-

- ## References

-

- See the `references/` directory for detailed guidance on:

- - Patient cohort analysis and stratification methods

- - Treatment recommendation development

- - Clinical decision algorithms

- - Biomarker classification and interpretation

- - Outcome analysis and statistical methods

- - Evidence synthesis and grading systems

-

- ## Templates

-

- See the `assets/` directory for LaTeX templates:

- - `cohort_analysis_template.tex` - Biomarker-stratified patient cohort analysis with statistical comparisons

- - `treatment_recommendation_template.tex` - Evidence-based clinical practice guidelines with GRADE grading

- - `clinical_pathway_template.tex` - TikZ decision algorithm flowcharts for treatment sequencing

- - `biomarker_report_template.tex` - Molecular subtype classification and genomic profile reports

-

- **Template Features:**

- - 0.5in margins for compact presentation

- - Color-coded recommendation boxes

- - Professional tables for demographics, biomarkers, outcomes

- - Built-in support for Kaplan-Meier curves, waterfall plots, forest plots

- - GRADE evidence grading tables

- - Confidentiality headers for pharmaceutical documents

-

- ## Scripts

-

- See the `scripts/` directory for analysis and visualization tools:

- - `generate_survival_analysis.py` - Kaplan-Meier curve generation with log-rank tests, hazard ratios, 95% CI

- - `create_waterfall_plot.py` - Best response visualization for cohort analyses

- - `create_forest_plot.py` - Subgroup analysis visualization with confidence intervals

- - `create_cohort_tables.py` - Demographics, biomarker frequency, and outcomes tables

- - `build_decision_tree.py` - TikZ flowchart generation for treatment algorithms

- - `biomarker_classifier.py` - Patient stratification algorithms by molecular subtype

- - `calculate_statistics.py` - Hazard ratios, Cox regression, log-rank tests, Fisher's exact

- - `validate_cds_document.py` - Quality and compliance checks (HIPAA, statistical reporting standards)

- - `grade_evidence.py` - Automated GRADE assessment helper for treatment recommendations

-