Predictors of abnormal computed tomography findings for paediatric head injury: a retrospective cohort study
Bibliographic record
Abstract
Objectives: Head injuries in children are common causes for visits to the emergency department (ED). Computed tomography (CT) scans are useful for confirming head injury diagnoses. However, radiation exposure from CT scans might cause lethal malignancies. We aimed to examine predictors for the indication of performing CT scans necessary for diagnosis. Design: Retrospective cohort study. Setting: Three EDs in Japan Participants: Patients aged <16 years with head trauma who underwent CT. Primary and Secondary Outcome Measures: The primary outcome measure was abnormal CT findings that were evaluated using the area under the receiver-operating characteristic curve (AUC). We derived predictors from three existing CDRs: Canadian Assessment of Tomography for Childhood Head Injury (CATCH), Children’s Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE), and Paediatric Emergency Care Applied Research Network (PECARN). Results: Of 1,103 eligible patients, 410 were included in this study. There were 283 (68%) boys, and the median age was 2 years. In total, 35 (9%) patients showed an abnormality, 73 (18%) were admitted, and 3 (0.7%) underwent neurosurgery. We developed a CDR consisting of 6 predictors for identifying children with abnormal CT findings: (1) severe or worsening headache; (2) GCS <15; (3) signs of skull fracture; (4) hematoma; (5) loss of consciousness; and (6) altered mental status. Our CDR had a sensitivity of 74.3%, a specificity of 75.2%, a negative predictive value of 96.9%, and a positive predictive value of 21.8%. The AUC for our rule was not inferior to those for CATCH, CHALICE, and PECARN {0.75 (95% confidence interval [CI], 0.67-0.81) versus 0.64 (95% CI, 0.56-0.73; p<0.05), 0.68 (95% CI, 0.60–0.76; p=0.28), and 0.67 (95% CI, 0.60-0.74); p=0.10}. Conclusions: Our findings suggest that a CDR, which lowers the frequency of CT in children with head injuries, must be developed and validated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".