Does it support statistical analysis for clinical trials?

Yes, it supports key statistical metrics including hazard ratios, p-values, confidence intervals, Kaplan-Meier survival curves, and Cox regression analysis.

Does the skill generate visual treatment algorithms?

Yes, it is designed to work with the scientific-schematics skill to automatically generate flowcharts, decision trees, and patient flow diagrams for every document.

Can this skill be used for individual patient treatment at the bedside?

No, this skill is specifically designed for group-level cohort analysis and evidence synthesis in research or pharmaceutical settings. For individual patient plans, use the treatment-plans skill.

How does it handle biomarker integration?

The skill integrates genomic alterations (mutations, CNV, fusions), gene expression signatures, and IHC markers (like PD-L1) to stratify patients and correlate them with clinical outcomes.

What document formats does this skill produce?

The skill generates publication-ready documents in LaTeX format, which are optimized for high-quality PDF output suitable for regulatory submissions, medical affairs, and peer-reviewed journals.

Clinical Decision Support

Name: Clinical Decision Support
Author: K-Dense-AI

byK-Dense-AI

•

2,188

데이터 과학 및 ML

Generates professional, publication-ready clinical decision support documents and biomarker-stratified cohort analyses for pharmaceutical and clinical research.

소개

This skill enables Claude to produce high-fidelity, evidence-based clinical decision support (CDS) documents tailored for pharmaceutical companies, researchers, and medical societies. It specializes in complex patient cohort analysis featuring statistical outcomes like survival curves and hazard ratios, alongside treatment recommendation reports utilizing the GRADE evidence grading system. By integrating biomarker data and generating automated visual decision algorithms via LaTeX, it streamlines the creation of regulatory submissions, clinical guidelines, and drug development documentation.

주요 기능

2,188 GitHub stars
Visual clinical decision algorithms and biomarker stratification trees via schematics
Biomarker-stratified patient cohort analysis with statistical outcome comparisons
Integration of complex metrics including OS, PFS, hazard ratios, and waterfall plots
Evidence-based treatment guidelines using the standardized GRADE grading system
Automated generation of publication-ready LaTeX and PDF medical documents

사용 사례

Real-world evidence (RWE) synthesis and comparative effectiveness studies using EMR data
Development of evidence-based treatment algorithms for medical societies and journals
Phase 2/3 clinical trial subgroup analysis and regulatory IND/NDA documentation

소개

주요 기능

2,188 GitHub stars
Visual clinical decision algorithms and biomarker stratification trees via schematics
Biomarker-stratified patient cohort analysis with statistical outcome comparisons
Integration of complex metrics including OS, PFS, hazard ratios, and waterfall plots
Evidence-based treatment guidelines using the standardized GRADE grading system
Automated generation of publication-ready LaTeX and PDF medical documents

사용 사례

Real-world evidence (RWE) synthesis and comparative effectiveness studies using EMR data
Development of evidence-based treatment algorithms for medical societies and journals
Phase 2/3 clinical trial subgroup analysis and regulatory IND/NDA documentation