Clinical Decision Rules for Paediatric Minor Head Injury: Are CT Scans a Necessary Evil?
Bibliographic record
Abstract
INTRODUCTION: High performing clinical decision rules (CDRs) have been derived to predict which head-injured child requires a computed tomography (CT) of the brain. We set out to evaluate the performance of these rules in the Singapore population. MATERIALS AND METHODS: This is a prospective observational cohort study of children aged less than 16 who presented to the emergency department (ED) from April 2014 to June 2014 with a history of head injury. Predictor variables used in 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 were collected. Decisions on CT imaging and disposition were made at the physician's discretion. The performance of the CDRs were assessed and compared to current practices. RESULTS: A total of 1179 children were included in this study. Twelve (1%) CT scans were ordered; 6 (0.5%) of them had positive findings. The application of the CDRs would have resulted in a significant increase in the number of children being subjected to CT (as follows): CATCH 237 (20.1%), CHALICE 282 (23.9%), PECARN high- and intermediate-risk 456 (38.7%), PECARN high-risk only 45 (3.8%). The CDRs demonstrated sensitivities of: CATCH 100% (54.1 to 100), CHALICE 83.3% (35.9 to 99.6), PECARN 100% (54.1 to 100), and specificities of: CATCH 80.3% (77.9 to 82.5), CHALICE 76.4% (73.8 to 78.8), PECARN high- and intermediate-risk 61.6% (58.8 to 64.4) and PECARN high-risk only 96.7% (95.5 to 97.6). CONCLUSION: The CDRs demonstrated high accuracy in detecting children with positive CT findings but direct application in areas with low rates of significant traumatic brain injury (TBI) is likely to increase unnecessary CT scans ordered. Clinical observation in most cases may be a better alternative.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".