A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study
Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases. Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions…
Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.
Evidence
- Peer-reviewedMedicina Clínica2026-08-21
How should this claim be treated?
Truvace Impact Record TRV-2026-0857, v1: “A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study.” Truvace, 2026-08-23. /record/TRV-2026-0857 (accessed at citation time). sha256 0543d556a50b9426…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0857 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.
ace