clinical-decision-support

To generate a clinical decision support PDF, this skill synthesizes biomarker-stratified patient cohort analyses and applies GRADE evidence grading to treatment recommendations, outputting a publication-ready LaTeX document with an executive summary and clinical schematics.

Yes, you can generate patient cohort reporting with survival analysis by utilizing built-in libraries like lifelines and matplotlib to calculate hazard ratios and render survival curves, which are then embedded into the final clinical decision support PDF.

Yes, this skill processes biomarker data like PD-L1 or HER2 to generate patient cohort narratives stratified by molecular subtype, providing statistical outcome reporting including OS, PFS, and ORR metrics for clinical research settings.

The best way to structure guideline-style treatment recommendations is by using the mandatory LaTeX format, which places an executive summary on page one and follows with GRADE-graded decision algorithms and generated clinical schematics to support visual decision pathways.

Yes, a LaTeX environment is required to compile the mandatory publication-ready PDF structure, which includes the executive summary on page one, generated clinical schematics, and figures supporting the decision pathways and visual clarity.