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Automated Calculation of the Pediatric Emergency Care Applied Research Network (PECARN) Intra-Abdominal Injury Rule from Unstructured Emergency Department Notes Using a Large Language Model

Objective: The Pediatric Emergency Care Applied Research Network (PECARN) intra-abdominal injury clinical prediction rule identifies children at very low risk for clinically significant intra-abdominal injury after blunt abdominal trauma. However, retrospective evaluation of the rule's use in practice requires resource-intensive chart review. We investigated whether large language models (LLMs) can automatically calculate PECARN intra-abdominal injury risk scores from real-world unstructured emergency department…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1829· cite & verify
Automated Calculation of the Pediatric Emergency Care Applied Research Network (PECARN) Intra-Abdominal Injury Rule from Unstructured Emergency Department Notes Using a Large Language Model
Doctor Wenlock outside St Thomas's Hospital - geograph.org.uk - 3101668 by PAUL FARMER. CC BY-SA 2.0 · https://creativecommons.org/licenses/by-sa/2.0

In brief

Researchers tested whether OpenAI o1 could compute the PECARN intra-abdominal injury risk score for children after blunt abdominal trauma by reading physician notes written in free text. They compared its output against scores derived by clinicians, using records from two urban pediatric emergency departments.

The result suggests that clinical prediction rules could be calculated automatically from documentation, easing the labor of retrospective audits of how such rules are applied. The evidence comes from a retrospective analysis of a small sample, so performance in prospective care or other settings remains untested.

Main points

  1. The study analyzed 196 children with complete ED notes after blunt abdominal trauma.
  2. Binary risk scores reached 94.4% sensitivity and 98.2% specificity against clinician-derived standards.
  3. Multicategorical agreement by weighted Cohen's kappa was 0.96.
  4. Extraction of individual PECARN predictor variables had accuracy above 94%.

The gain

An LLM read unstructured emergency notes and produced PECARN intra-abdominal injury risk scores closely matching clinician-derived scores, which could reduce the chart review needed to evaluate how the rule is used.

The rundown

The PECARN rule identifies children at very low risk for clinically significant intra-abdominal injury after blunt abdominal trauma. Evaluating whether clinicians use it in practice normally requires resource-intensive chart review, which motivated the test of an automated approach.

The study was a retrospective secondary analysis at two urban pediatric emergency departments. OpenAI o1 was compared with clinician-derived standards both for the final risk scores and for extracting each predictor variable from unstructured physician notes.

Reported accuracy was 95.4% with an F1 score of 0.97. The authors conclude that the LLM performed comparably to clinicians and that LLMs may enable automated calculation of clinical prediction rules from clinical documentation.

Sources

  1. Peer-reviewedJACEP Open2026-10-08

The debate