clinical-decision-support
为制药和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含结局)和治疗推荐报告(基于证据的指南并含决策算法)。支持 GRADE 证据分级、统计分析(风险比、生存曲线、瀑布图)、生物标志物整合和法规合规。输出适合药物开发、临床研究和证据合成的可直接发表的 LaTeX/PDF 格式。
机器翻译查看英文原文
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.
适用对象:PDF。自动收录 · 待核查。
原作者说明节选
从固定版本的 SKILL.md 提取,保留原文语言。以下内容属于作者说明,本站尚未验证安装、兼容或运行结果。
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原文涵盖的主题
- Clinical Decision Support Documents
- Description
- Capabilities
- Document Types
- Clinical Features
- Pharmaceutical and Research Use Cases
- When to Use
- Visual Enhancement with Scientific Schematics
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 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. 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
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- 来源仓库
- davila7/claude-code-templates
- 源文件
- cli-tool/components/skills/scientific/clinical-decision-support/SKILL.md
- 固定版本
- 21be197f100a12b8d0dd9a211cc244c8179f84d3
来源与内容状态
本条为自动发现的元数据,尚未完成人工文档核查;适用性与运行结果待核查。
原作者:davila7/claude-code-templates · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-09T15:01:39.948Z。
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