Applicability of the CATCH, CHALICE and PECARN paediatric head injury clinical decision rules: pilot data from a single Australian centre
Bibliographic record
Abstract
BACKGROUND: Clinical decision rules (CDRs) for paediatric head injury (HI) exist to identify children at risk of traumatic brain injury. Those of the highest quality are the Canadian assessment of tomography for childhood head injury (CATCH), Children's head injury algorithm for the prediction of important clinical events (CHALICE) and Pediatric Emergency Care Applied Research Network (PECARN) CDRs. They target different cohorts of children with HI and have not been compared in the same setting. We set out to quantify the proportion of children with HI to which each CDR was applicable. METHODS: Consecutive children presenting to an Australian paediatric Emergency Department with HIs were enrolled. Published inclusion/exclusion criteria and predictor variables from the CDRs were collected prospectively. Using these we determined the frequency with which each CDR was applicable. RESULTS: 1012 patients (69.9%) were enrolled with 949 available for analysis. Mean age was 6.8 years (21% <2 years). 95% had initial Glasgow Coma Scale 15. CT rate was 12.8% and neurosurgery rate was 0.7%. No CDR was applicable to all patients. CHALICE was applicable to the most (97%, 95% CI 96% to 98%) and CATCH to the fewest (26%, 95% CI 24% to 29%). PECARN was applicable to 76% (95% CI 70% to 82%) aged <2 years, and 74% (95% CI 71% to 77%) aged 2-<18 years. CONCLUSIONS: Each CDR is applicable to a different proportion of children with HI. This makes a direct comparison of the CDRs difficult. Prior to selection of any for implementation they should undergo validation outside the derivation setting coupled with an analysis of their performance accuracy, usability and cost effectiveness.
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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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".