− HEALTH There is a growing literature on the prediction of risk of deterioration in hospital settings, including by l…− SCIENCE Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity o…+ HEALTH Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed gene…− HEALTH Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissectio…− HEALTH Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but out…− HEALTH Background Large language model (LLM)-generated hospital courses are increasingly integrated into electronic…− HEALTH Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporati…+ HEALTH Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (…
TruaceTracing the truth around AIFriday, September 4, 2026
Subscribe
TRV-2026-0977Certified recordPeer-reviewed

A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics

Background Large language model (LLM)-generated hospital courses are increasingly integrated into electronic health records (EHRs), yet their accuracy and safety in pediatric populations remain poorly characterized. Objective To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation. Methods We conducted a descriptive evaluation from June 10 to August 8, 2025, at an academic freestandi…

Health · P Space — documented harm · certified 2026-09-04 · v1 · article view · machine-readable

Current reading — problem

Objective To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0977, v1: “A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics.” Truvace, 2026-09-04. /record/TRV-2026-0977 (accessed at citation time). sha256 55a344ced3fbdf0a…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv155a344ced3fb…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0977 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.