clinical-decision-support

You can generate publication-ready clinical decision support documents by analyzing biomarker-stratified cohorts and outputting LaTeX/PDF files with GRADE-graded recommendations and CONSORT/STROBE-aligned language. This Skill automates combining survival metrics with evidence synthesis.

Yes, you can create GRADE-based treatment recommendations and decision algorithms formatted for regulatory submissions. The Skill integrates evidence grading with decision algorithms and applies regulatory-ready formatting to your clinical outputs.

You stratify patient cohorts by biomarker and calculate survival metrics using the lifelines and scipy dependencies. The Skill processes population-level analyses to produce outcome metrics that drive your evidence synthesis.

Yes, this Skill supports generating TikZ flowcharts for clinical decision algorithms directly in LaTeX. It produces publication-ready documents that include decision-tree visualizations aligned with clinical guideline formatting standards.

The best way to prepare biomarker-informed cohort data is to structure it using pandas and numpy for population-level analysis. This ensures your dataset captures the survival and outcome metrics required for GRADE-based recommendations.

You need a LaTeX environment to compile the regulatory-ready PDFs generated by this Skill. The Skill produces the LaTeX source code and TikZ flowcharts, but the actual PDF compilation requires an external LaTeX distribution.