The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2870
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T18:03:20.533261Z
status: published
lens: trace
sector: health
headline: Comparative performance and safety of three large language models in pediatric paracetamol poisoning management
dek: Abstract Background Paracetamol overdose is a common reason for pediatric poisoning consultations and emergency department visits, and. Large language models (LLMs) are increasingly being explored for clinical decision support, but evidence on pediatric poisoning is scarce. We compared the performance and safety of three contemporary LLMs for managing pediatric paracetamol poisoning using guideline-based simulated scenarios. Methods In this cross-sectional, comparative, scenario-based study, 30 hypothetical pedi…
gain_title: In a study of 30 simulated pediatric paracetamol poisoning cases, Claude Sonnet 4.6 scored 24.50 of 25 on expert-rated quality, made no critical safety errors and matched the reference guideline in all 30 responses.
problem_title: In the same 30-scenario test of pediatric paracetamol poisoning, Gemini 3 Flash made critical safety errors in 3 of 30 responses and ChatGPT 5.5 in 1 of 30, including missed lab checks and risk of stopping N-acetylcysteine early.
trace_subject: AI chatbot management of pediatric paracetamol poisoning scenarios
gain_reading: In a study of 30 simulated pediatric paracetamol poisoning cases, Claude Sonnet 4.6 scored 24.50 of 25 on expert-rated quality, made no critical safety errors and matched the reference guideline in all 30 responses.
gain_evidence: Claude Sonnet 4.6 produced no critical safety errors and was concordant with the reference standard in all responses | Claude Sonnet 4.6 achieved significantly higher total AIES-CPV (24.50 ± 1.43)
problem_reading: In the same 30-scenario test of pediatric paracetamol poisoning, Gemini 3 Flash made critical safety errors in 3 of 30 responses and ChatGPT 5.5 in 1 of 30, including missed lab checks and risk of stopping N-acetylcysteine early.
problem_evidence: one critical error occurred with ChatGPT 5.5 (1/30) and three with Gemini 3 Flash (3/30)
quick_read: A scenario-based study tested ChatGPT 5.5, Claude Sonnet 4.6 and Gemini 3 Flash on 30 hypothetical pediatric paracetamol poisoning cases, yielding 90 responses. A blinded panel of two general pediatricians and one pediatric emergency specialist scored each one against a guideline-derived reference algorithm. Claude scored 24.50 out of 25 on the study's quality rubric, versus 21.00 for ChatGPT and 20.07 for Gemini.

Claude also scored 7.93 of 8 on the key-action checklist, against 7.13 for ChatGPT and 6.90 for Gemini, and 4.90 of 5 on the Global Quality Scale. It had no critical safety errors, while ChatGPT had one and Gemini had three. That error-rate difference was not statistically significant, and the authors say the test was underpowered.

The scenarios were hypothetical and the scores came from a single panel consensus using a rubric that has not been externally validated. The authors conclude chatbots may serve as adjunct decision support in pediatric toxicology but only under expert supervision with clinical verification.
limitation: Responses were scored by a single three-clinician consensus without inter-rater reliability, using an unvalidated rubric, on hypothetical cases with specific model versions on one date; critical errors were too rare (0–3 of 30) for the comparison to be statistically conclusive.
tag: Dual reading
key_points: The study used 30 scenarios written by pediatricians, covering acute, delayed, unknown-dose, repeated, modified-release, malnutrition and co-ingestion cases. | Every model got the same single-turn prompt in its public browser interface, with no follow-ups, plug-ins or browsing. | Claude got every N-acetylcysteine indication right (30/30), versus 22/30 for ChatGPT and 24/30 for Gemini. | Claude got the mg/kg dose right in 30/30 cases; ChatGPT managed 26/30 and Gemini 22/30. | ChatGPT and Gemini did not differ significantly on total quality score. | Claude's lead was widest in advanced decisions such as NAC stopping and acidosis, where it scored 25 in all six scenarios against a median of 18 for ChatGPT and 22 for Gemini. | Concordance with the reference standard was 30/30 for Claude, 26/30 for ChatGPT and 27/30 for Gemini.
rundown: The scenarios were developed from the Australia and New Zealand paracetamol poisoning guidelines plus pediatric emergency guidance from the Royal Children's Hospital Melbourne and Queensland Paediatric Emergency Care. Responses were anonymized and randomized before a three-clinician panel scored them by consensus. Scoring used a five-domain rubric, an eight-item checklist, the Global Quality Scale and a critical-error flag.

Critical errors were defined as omissions that could affect safety, such as missing an NAC indication or omitting liver tests in delayed presentation. ChatGPT's one error was missing lab evaluation in repeated supratherapeutic ingestion. Gemini's three involved inadequate evaluation of an unknown-amount ingestion, missed lab evaluation in repeated ingestion and risk of premature NAC termination.

By category, Claude led in acute, atypical, special-situation and advanced scenarios. Gemini's scores varied most in unknown-time or delayed-presentation cases. Checklist results hit a ceiling for Claude, limiting how well the test separated the models.
sources:
- peer_reviewed | BMC Pediatrics | https://doi.org/10.1186/s12887-026-07949-2 | 2026-11-11
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

77166f069142db3dcb333ce4faf4f0d07c9032355d3d74ad08bef75104b0e435

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000