The Use of PECARN and CATCH Rules in Children With Minor Head Trauma Presenting to Emergency Department 24 Hours After Injury
Bibliographic record
Abstract
OBJECTIVE: Major studies (PECARN [Pediatric Emergency Care Applied Research Network], CATCH [Canadian Assessment of Childhood Head Injury]) that regulate the use of computed tomography (CT) algorithms in children with minor head trauma (MHT) have been conducted among children presenting in 24 hours after injury. In this study, we aimed to compare use and results of PECARN and CATCH rules in children presenting in and after 24 hours following injury. METHODS: Records of children who were admitted to emergency department and underwent CT imaging because of MHT during a 5-year period were retrospectively reviewed. Efficacy of PECARN and CATCH rules was investigated for predicting traumatic CT findings in patients presenting in and after 24 hours. Logistic regression was performed to evaluate whether presenting after 24 hours affected the ability of guidelines in predicting traumatic CT findings. RESULTS: This study included 2490 patients who met the criteria. Of these patients, 6.7% (168/2490) presented after 24 hours following injury. Traumatic CT findings were found in 6.7% (168/2490) of patients. This rate was 6.9% (161/2322) in those presenting in 24 hours and 4.2% (7/168) in those presenting after 24 hours, and there was no significant difference in the incidence of traumatic CT findings between the 2 groups (P = 0.17). Among children presenting in 24 hours, the sensitivity of PECARN was 96.3% (95% confidence interval [CI], 91.7%-98.5%), whereas the sensitivity of CATCH was 91.9% (95% CI, 86.3%-95.4%) in detecting traumatic intracranial injury. The sensitivity of both PECARN and CATCH was 85.7% (95% CI, 42.0%-99.2%) among children presenting after 24 hours. Presence of CT scan indication according to PECARN statistically predicted intracranial damage, and this was not affected by the admission time. CONCLUSIONS: Patients with MHT presenting after 24 hours following injury constitute a clinically important population. Regardless of the admission time, current guidelines predict traumatic CT abnormalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".