scientific-skills/clinical-decision-support/SKILL.md
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.
Patient Cohort Analysis
Treatment Recommendation Reports
This skill is specifically designed for pharmaceutical and clinical research applications:
Drug Development
Medical Affairs
Clinical Guidelines
Real-World Evidence
Use this skill when you need to:
Do NOT use this skill for:
treatment-plans skill)treatment-plans skill)treatment-plans skill)⚠️ 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.png
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
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.
The first page of every CDS document should contain ONLY the executive summary with the following components:
Required Elements (all on page 1):
Document Title and Type
Report Information Box (using colored tcolorbox)
Key Findings Boxes (3-5 colored boxes using tcolorbox)
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 3
Page 1 Executive Summary for Treatment Recommendations should include:
Detailed Sections (Page 3+):
MANDATORY FIRST PAGE REQUIREMENT:
Document Specifications:
Visual Elements:
This skill integrates with:
Clinical Decision Support (this skill):
Treatment-Plans Skill:
When to use each:
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.
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.
GRADE System
Recommendation Strength
\thispagestyle{empty} and end with \newpageSee the references/ directory for detailed guidance on:
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:
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