Native Probability Outputs for Clinical Triage: An Evaluation of Calibration and Decision Accuracy

Liang You, Zhiyin Zhou, Dawei Tang, Chuchu Wu, Jingzhuo Hu

Patient-facing triage requires an appropriate care recommendation and recognition of when information is insufficient. We evaluate whether Jev, a decision model that returns probabilities over predefined actions, supports both judgments. Using CARE-Bench, we compare Jev with four general-purpose language models and three input-independent baselines across 925 decision points from 439 public cases. On the public test subset, Jev ranks fourth of five models by macro-F1 (0.461) but has the lowest observed multiclass Brier score (0.610), an 18.7\% improvement over uniform prediction (case-bootstrap 95\% interval, 4.9--31.1\%). Noul has lower binary Brier than Choice (0.215 versus 0.276). A evaluation of 453 HealthBench conversations again gives Jev the lowest observed Brier score (0.299), but measures a distinct emergency-referral judgment. Jev combines lower probability error with missed clarification needs in the four-action task. Asking about information sufficiency separately increases recall but also produces frequent unnecessary clarification assignments.