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clinical-decision-support

Generate clinical decision support documents with biomarker-stratified cohort analyses and GRADE evidence grading.

2|Updated Jun 4, 2026
Search Tags:#statistics#latex#clinical#grade#biomarker#oncology#pharmaceutical
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill clinical-decision-support-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/clinical-decision-support
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill clinical-decision-support-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, lifelines, matplotlib, pyyaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill addresses the complexity of synthesizing clinical trial data, biomarker profiles, and treatment guidelines into professional, regulatory-ready documents for pharmaceutical and research environments.

Core Features & Use Cases

  • Patient Cohort Analysis: Perform biomarker-stratified group analyses with statistical outcomes like hazard ratios and survival curves.
  • Treatment Recommendation Reports: Generate evidence-based clinical guidelines using the GRADE system and decision algorithms.
  • Use Case: A clinical researcher needs to generate a 10-page report analyzing the efficacy of a drug in a cohort of 60 HER2-positive breast cancer patients, including Kaplan-Meier survival curves and GRADE-graded treatment recommendations.

Quick Start

Use the clinical-decision-support skill to analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression including ORR, PFS, and OS outcomes.

Frequently Asked Questions about clinical-decision-support

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate clinical decision support reports with GRADE evidence grading and biomarker stratification?▼

To generate clinical decision support reports, you input patient cohort data to produce evidence-based treatment recommendations using GRADE grading and biomarker-stratified statistical analyses. The skill outputs publication-ready PDF documents via LaTeX templates.

Can I create Kaplan-Meier survival curves for oncology cohorts stratified by biomarker expression?▼

Yes, you can create Kaplan-Meier survival curves for oncology cohorts stratified by biomarker expression. The skill uses the lifelines Python library to analyze patient cohorts and calculate statistical outcomes like hazard ratios and survival curves.

What Python dependencies do I need for statistical analysis and LaTeX report generation?▼

You need pandas, numpy, scipy, lifelines, matplotlib, and pyyaml installed for statistical analysis and LaTeX report generation. These dependencies support biomarker-stratified group analyses, survival curve plotting, and publication-ready PDF output creation.

Does this tool support pharmaceutical regulatory submissions and clinical guideline development?▼

Yes, this tool supports pharmaceutical regulatory submissions and clinical guideline development. It synthesizes clinical trial data, biomarker profiles, and treatment guidelines into professional, regulatory-ready documents using statistical analysis and GRADE evidence grading.

How do I analyze patient cohort efficacy outcomes like ORR, PFS, and OS in NSCLC patients?▼

You analyze patient cohort efficacy outcomes by inputting clinical trial data stratified by biomarker profiles, such as PD-L1 expression in NSCLC patients. The skill calculates ORR, PFS, and OS outcomes using scipy and lifelines for statistical evaluation.

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