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 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 | 0.025 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".