Evaluation of four clinical decision rules in children with minor head trauma: NEXUS II, PECARN, CHALICE, and CATCH
Bibliographic record
Abstract
Background: Clinical decision rules could potentially help emergency department (ED) trauma triage, allowing clinicians to prioritize treatment for the most severely injured patients.Objectives: This study evaluated and compared the diagnostic accuracy of the National Emergency X-radiography Utilization Study II (NEXUS II), the Pediatric Emergency Care Applied Research Network (PECARN), the Canadian Assessment of Tomography for Childhood Head Injury (CATCH), and the Children’s Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE) in identifying intracranial injury (ICI) in children with minor head trauma.Methods: This prospective, cross-sectional, descriptive-comparative study was conducted on children with mild head trauma who presented to the ED. Inclusion criteria were age under 16 years, Glasgow Coma Scale (GCS) score of ≥14, and the requirement of a head Computed Tomography (CT) scan as a part of their examination. The primary outcome was the presence of pathological findings on the CT scan. The predictive value of the four rules was evaluated using the receiver operating characteristic (ROC) analysis.Results: Among the 340 children studied, 25 (7.4%) had an intracranial injury, and six patients (1.8%) required neurosurgical intervention. The mean age was 7.96 ± 4.11 years (60.9% boys). The sensitivities for predicting a positive head CT were 96.0% (95%CI 79.6– 99.9) for the NEXUS II and PECARN rules, and 92.0% (95%CI 73.9–99.0) for the CATCH and CHALICE rules. Additionally, the negative predictive values (NPV) for these rules were 99.5% (95%CI 96.4–99.9) for the NEXUS II, 99.4% (95%CI 96.2–99.9) for the PECARN, and 98.8% (95%CI 95.8–99.7) for the CATCH and 98.8% (95%CI 95.7–99.7) for the CHALICE. Notably, one of the 25 patients who had pathologic findings on CT met none of the diagnostic criteria of all the rules and did not require neurosurgical intervention.Conclusion: All four clinical decision rules demonstrated strong accuracy in identifying pediatric patients with abnormal CT findings, showing excellent sensitivities and NPVs, which supports their suitability for evaluating mild head trauma in the ED.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".