Blood-borne virus prediction models get a retrieval-grounded bias check: ARBITER matches a human statistician and a general LLM against expert consensus
Risk-of-bias appraisal of blood-borne virus prediction models has depended on reviewers working through PROBAST+AI signalling questions by hand, which is slow and varies between assessors. ARBITER grounds each judgement in retrieved PROBAST+AI criteria, statistical methods and disease-specific knowledge, and separates model-development from model-evaluation pathways. Across 200 signalling-question observations from nine HIV, HBV and HCV prediction-model studies, ARBITER's weighted kappa against expert consensus was 0.701, versus 0.724 for a manual reviewer and 0.714 for Claude Sonnet 4.6, with overlapping confidence intervals.