clinical-decision-support
为药品与临床研究场景生成专业的临床决策支持（CDS）文档，包括患者队列分析（按生物标志物分层并关联结局）以及治疗推荐报告（基于证据的指南与决策算法）。支持GRADE证据分级、统计分析（风险比、生存曲线、瀑布图）、生物标志物整合与监管合规。输出可直接用于发表的LaTeX/PDF格式，针对药物开发、临床研究与证据综合进行优化。
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name:clinical-decision-supportdescription: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 Bashlicense:MIT Licenserequired_environment_variables:[object Object]metadata:[object Object]
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:
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
Treatment Recommendation Reports
Clinical Features
Pharmaceutical and Research Use Cases
This skill is specifically designed for pharmaceutical and clinical research applications:
Drug Development
Medical Affairs
Clinical Guidelines
Real-World Evidence
When to Use
Use this skill when you need to:
Do NOT use this skill for:
treatment-plans skill)treatment-plans skill)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:
How to generate figures:
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.pngThe AI will automatically:
When to add schematics:
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):
- Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations")
- Subtitle with disease state and focus
- Document type and purpose
- Date of analysis/report
- Disease state and patient population
- Author/institution (if applicable)
- Analysis framework or methodology
- 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:
\thispagestyle{empty} to remove page numbers from page 1\newpage)\newpage before table of contents or detailed sectionsExample First Page LaTeX Structure:
\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 3Patient Cohort Analysis (Detailed Sections - Page 3+)
Treatment Recommendation Reports (Detailed Sections - Page 3+)
Page 1 Executive Summary for Treatment Recommendations should include:
Detailed Sections (Page 3+):
Output Format
MANDATORY FIRST PAGE REQUIREMENT:
Document Specifications:
Visual Elements:
- 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)
- 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
- 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
Integration
This skill integrates with:
Key Differentiators from Treatment-Plans Skill
Clinical Decision Support (this skill):
Treatment-Plans Skill:
When to use each:
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
Outcome Metrics
Statistical Methods
Evidence Grading
GRADE System
Recommendation Strength
Best Practices
For Cohort Analyses
- 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
- Response: RECIST 1.1, iRECIST for immunotherapy
- Adverse events: CTCAE version 5.0
- Performance status: ECOG or Karnofsky
- 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
For Treatment Recommendation Reports
- Use GRADE system consistently (1A, 1B, 2A, 2B, 2C)
- Document rationale for each grade
- Clearly state quality of evidence (high, moderate, low, very low)
- 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)
- Link specific biomarkers to therapy recommendations
- Specify testing methods and validated assays
- Include FDA/EMA approval status for companion diagnostics
General Best Practices
- 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
References
See the references/ directory for detailed guidance on:
Templates
See the assets/ directory for LaTeX templates:
cohort_analysis_template.tex - Biomarker-stratified patient cohort analysis with statistical comparisonstreatment_recommendation_template.tex - Evidence-based clinical practice guidelines with GRADE gradingclinical_pathway_template.tex - TikZ decision algorithm flowcharts for treatment sequencingbiomarker_report_template.tex - Molecular subtype classification and genomic profile reportsTemplate Features:
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% CIcreate_waterfall_plot.py - Best response visualization for cohort analysescreate_forest_plot.py - Subgroup analysis visualization with confidence intervalscreate_cohort_tables.py - Demographics, biomarker frequency, and outcomes tablesbuild_decision_tree.py - TikZ flowchart generation for treatment algorithmsbiomarker_classifier.py - Patient stratification algorithms by molecular subtypecalculate_statistics.py - Hazard ratios, Cox regression, log-rank tests, Fisher's exactvalidate_cds_document.py - Quality and compliance checks (HIPAA, statistical reporting standards)grade_evidence.py - Automated GRADE assessment helper for treatment recommendations