Validation and refinement of a clinical decision rule for the use of computed tomography in children with minor head injury in the emergency department
Bibliographic record
Abstract
BACKGROUND: There is uncertainty about which children with minor head injury need to undergo computed tomography (CT). We sought to prospectively validate the accuracy and potential for refinement of a previously derived decision rule, Canadian Assessment of Tomography for Childhood Head injury (CATCH), to guide CT use in children with minor head injury. METHODS: This multicentre cohort study in 9 Canadian pediatric emergency departments prospectively enrolled children with blunt head trauma presenting with a Glasgow Coma Scale score of 13-15 and loss of consciousness, amnesia, disorientation, persistent vomiting or irritability. Phys icians completed standardized assessment forms before CT, including clinical predictors of the rule. The primary outcome was neurosurgical intervention and the secondary outcome was brain injury on CT. We calculated test characteristics of the rule and used recursive partitioning to further refine the rule. RESULTS: Of 4060 enrolled patients, 23 (0.6%) underwent neurosurgical intervention, and 197 (4.9%) had brain injury on CT. The original 7-item rule (CATCH) had sensitivities of 91.3% (95% confidence interval [CI] 72.0%-98.9%) for neurosurgical intervention and 97.5% (95% CI 94.2%-99.2%) for predicting brain injury. Adding "≥ 4 episodes of vomiting" resulted in a refined 8-item rule (CATCH2) with 100% (95% CI 85.2%-100%) sensitivity for neurosurgical intervention and 99.5% (95% CI 97.2%-100%) sensitivity for brain injury. INTERPRETATION: Among children presenting to the emergency department with minor head injury, the CATCH2 rule was highly sensitive for identifying those children requiring neurosurgical intervention and those with any brain injury on CT. The CATCH2 rule should be further validated in an implementation study designed to assess its clinical impact.
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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 | 0.029 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".