Variation in Utilization of Computed Tomography Scanning for the Investigation of Minor Head Trauma in Children: A Canadian Experience
Bibliographic record
Abstract
OBJECTIVES: To compare the utilization rates of CT scans in investigating minor head trauma in children in Canada, to identify the injuries determined by these scans, and to identify clinical findings that are highly associated with its diagnosis and the injury itself. METHODS: A retrospective cohort study involving nine pediatric hospitals in Canada was conducted. A structured data collection method was used. Inclusion criteria included age 16 years or less, history of blunt head trauma, and a Glasgow Coma Scale score (GCS) greater than or equal to 13. Data collected included demographic information, type of injury, relevant clinical information, computed tomography (CT) scan data, and clinical outcome. Clinical findings associated with CT scan and positive CT scan were identified using logistic regression. RESULTS: One thousand one hundred sixty-four children were included in the study. One hundred seventy-one (15%) had a CT scan, of which 60 (35%) were abnormal. There was a significant difference in the rate of ordering of CT scans among the participating hospitals, but no significant difference in the rate of abnormal CT scans. Mechanism of injury, GCS, and loss of consciousness were significantly related to the presence of an abnormal CT scan. CONCLUSIONS: Although there is a significant difference in the utilization of CT scans to investigate minor head trauma in children across Canada, there is no significant difference in the frequency of head injuries in these patients. This suggests that it may be possible to determine clinical criteria that are predictive of a head injury in these patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".