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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).
One skill from scientific-agent-skills .
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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). 📦 Ships with scientific-agent-skills
</> SKILL.md
clinical-decision-support.SKILL.md
---
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
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
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
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:**
```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 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
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