Comparing CATCH, CHALICE and PECARN clinical decision rules for paediatric head injuries
Bibliographic record
Abstract
Many children present to emergency departments following head injury (HI), with a small number at risk of avoidable poor outcome. Difficulty identifying such children, coupled with increased availability of cranial CT, has led to variation in practice and increased CT rates. Clinical decision rules (CDRs) have been derived for paediatric HI but there is no published comparison to assist in deciding which to implement. The content of the three of highest quality and accuracy are described and compared. Systematic reviews of paediatric HI CDRs were published in 2009 and 2011. To identify CDRs published since the most recent review, key databases were searched, selecting studies which included CDRs involving children aged 0-18 years with a history of HI. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies Tool, and performance evaluated by reported accuracy. Three high quality CDRs were identified: CATCH (Canadian Assessment of Tomography for Childhood Head Injury) CHALICE (Children's Head Injury Algorithm for the Prediction of Important Clinical Events) and PECARN (Paediatric Emergency Care Applied Research Network). All were derived with high methodological standards but differed in key areas, including study population, outcomes and severity of HI. Each stated different predictor variables and only PECARN provided a separate algorithm for young children. CATCH and CHALICE identify children requiring CT and PECARN those who do not. All perform with high sensitivity and low specificity. PECARN is the only validated CDR, and none has undergone impact analysis. These three CDRs should undergo validation and comparison in a single population, with analysis of their impact on practice and financial implications, to aid relevant bodies in deciding which to implement.
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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 | 0.050 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".