CATCH: a clinical decision rule for the use of computed tomography in children with minor head injury
Bibliographic record
Abstract
BACKGROUND: There is controversy about which children with minor head injury need to undergo computed tomography (CT). We aimed to develop a highly sensitive clinical decision rule for the use of CT in children with minor head injury. METHODS: For this multicentre cohort study, we enrolled consecutive children with blunt head trauma presenting with a score of 13-15 on the Glasgow Coma Scale and loss of consciousness, amnesia, disorientation, persistent vomiting or irritability. For each child, staff in the emergency department completed a standardized assessment form before any CT. The main outcomes were need for neurologic intervention and presence of brain injury as determined by CT. We developed a decision rule by using recursive partitioning to combine variables that were both reliable and strongly associated with the outcome measures and thus to find the best combinations of predictor variables that were highly sensitive for detecting the outcome measures with maximal specificity. RESULTS: Among the 3866 patients enrolled (mean age 9.2 years), 95 (2.5%) had a score of 13 on the Glasgow Coma Scale, 282 (7.3%) had a score of 14, and 3489 (90.2%) had a score of 15. CT revealed that 159 (4.1%) had a brain injury, and 24 (0.6%) underwent neurologic intervention. We derived a decision rule for CT of the head consisting of four high-risk factors (failure to reach score of 15 on the Glasgow coma scale within two hours, suspicion of open skull fracture, worsening headache and irritability) and three additional medium-risk factors (large, boggy hematoma of the scalp; signs of basal skull fracture; dangerous mechanism of injury). The high-risk factors were 100.0% sensitive (95% CI 86.2%-100.0%) for predicting the need for neurologic intervention and would require that 30.2% of patients undergo CT. The medium-risk factors resulted in 98.1% sensitivity (95% CI 94.6%-99.4%) for the prediction of brain injury by CT and would require that 52.0% of patients undergo CT. INTERPRETATION: The decision rule developed in this study identifies children at two levels of risk. Once the decision rule has been prospectively validated, it has the potential to standardize and improve the use of CT for children with minor head injury.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".