Republished: Pediatric Emergency Care Applied Research Network head injury clinical prediction rules are reliable in practice
Bibliographic record
Abstract
OBJECTIVE: The Pediatric Emergency Care Applied Research Network (PECARN) traumatic brain injury (TBI) age-based clinical prediction rules identify children at very low risk of a significant head injury who can safely avoid CT. Our goal was to independently validate these prediction rules. DESIGN: Cross-sectional study. SETTING: Two paediatric emergency departments located in USA and in Italy. PATIENTS: All children presenting within 24 h of a head injury with a Glasgow Coma Score of ≥ 14. INTERVENTION: Assessment of PECARN TBI clinical predictors. MAIN OUTCOME MEASURE: Clinically important TBI defined as head injury resulting in death, intubation for >24 h, neurosurgery or two or more nights of hospitalisation for the management of head trauma. RESULTS: During the study period, we included 2439 children (91% of eligible patients), of which 959 (39%) were < 2 years of age and 1439 (59%) were male. Of the study patients, 373 (15%) had a CT performed, 69 (3%) had traumatic findings on their CT and 19 (0.8%) had a clinically important TBI. None of the children with a clinically important TBI were classified as very low risk by the PECARN TBI prediction rules (overall sensitivity 100%; 95% CI 83.2% to 100%, specificity 55%, 95% CI 52.5% to 56.6%, and negative predictive value 100%, 95% CI 99.6% to 100%). CONCLUSIONS: In our external validation, the age-based PECARN TBI prediction rules accurately identified children at very low risk for a clinically significant TBI and can be used to assist CT decision making for children with minor blunt head trauma.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.005 |
| 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".