MASLD-LLM: analysis plans, code, and benchmark results for LLM vs classical-ML fibrosis staging from cfRNA gene-expression prompts

Benchmark package for the study 'Can large language models read cell-free RNA expression panels for fibrosis staging in MASLD?' Twelve frontier LLMs were scored against classical machine-learning gates (logistic regression, HistGradientBoosting) on three task families - binary advanced fibrosis, 5-class staging, and ordinal 0-4 - using text-formatted cfRNA prompts derived from the top-200 MAD genes of the Ghent plasma cell-free transcriptome cohort (Poma-Soto et al.). This deposit contains the archived analysis plans, the run/deviation log (RUNLOG), the runner and scoring scripts, the corpus manifest (task/model/item identifiers), leaderboards with bootstrap confidence intervals, and figures. It does NOT contain per-sample gene-expression values or the full prompt texts: those derive from EGA-gated data (EGAD50000000775, Ghent DAC Data Usage Agreement).