What statistical metrics can this skill handle?

It supports hazard ratios, Kaplan-Meier survival curves, response rates (ORR, DCR), confidence intervals, and p-value comparisons across biomarker-stratified subgroups.

Does it include visual diagrams in the output?

Yes, it includes a mandatory requirement to generate at least 1-2 AI-powered scientific schematics, such as clinical decision algorithms or patient flow diagrams, using the scientific-schematics tool.

Is this skill intended for direct bedside patient care?

No, this skill is specifically designed for pharmaceutical research and population-level analysis. For individual patient treatment plans at the bedside, use the treatment-plans skill.

What formatting does the output use?

The skill generates publication-ready LaTeX code that compiles into professional PDF documents, featuring structured executive summaries and color-coded recommendation boxes.

Clinical Decision Support

Name: Clinical Decision Support
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Generates publication-ready clinical decision support documents, biomarker-stratified cohort analyses, and evidence-based treatment guidelines.

About

This skill empowers researchers and pharmaceutical professionals to create high-quality, professional clinical decision support (CDS) documents. It specializes in two main areas: patient cohort analysis with complex statistical outcomes—such as survival curves and hazard ratios—and treatment recommendation reports utilizing the GRADE evidence grading system. Designed for drug development and regulatory environments, it integrates biomarker data, produces publication-quality LaTeX/PDF files, and mandates the inclusion of visual clinical algorithms to ensure professional standards in medical communication.

Key Features

Support for regulatory compliance and clinical terminology (SNOMED-CT, LOINC).
Publication-ready LaTeX/PDF output with professional, compact formatting.
Biomarker-stratified cohort analysis with statistical outcome comparisons (OS, PFS, ORR).
3 GitHub stars
Evidence-based treatment guidelines using the GRADE evidence grading system.
Automated generation of scientific schematics, flowcharts, and treatment algorithms.

Use Cases

Creating evidence-based clinical practice guidelines for medical societies and advisory boards.
Developing Phase 2/3 trial subgroup analyses for IND/NDA regulatory submissions.
Synthesizing real-world evidence (RWE) into comparative effectiveness cohort reports.

About

Key Features

Support for regulatory compliance and clinical terminology (SNOMED-CT, LOINC).
Publication-ready LaTeX/PDF output with professional, compact formatting.
Biomarker-stratified cohort analysis with statistical outcome comparisons (OS, PFS, ORR).
3 GitHub stars
Evidence-based treatment guidelines using the GRADE evidence grading system.
Automated generation of scientific schematics, flowcharts, and treatment algorithms.

Use Cases

Creating evidence-based clinical practice guidelines for medical societies and advisory boards.
Developing Phase 2/3 trial subgroup analyses for IND/NDA regulatory submissions.
Synthesizing real-world evidence (RWE) into comparative effectiveness cohort reports.