Low-Value Computed Tomography for Children in the Emergency Department: A Repeated Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: To compare low-value computed tomography (CT) use during pediatric emergency department (ED) visits by hospital type and physician specialty. METHODS: Repeated cross-sectional study using linked databases from Ontario, Canada. We reviewed pediatric ED discharges from 2010 to 2019 for 5 diagnoses with recommendations against routine CT use: abdominal pain, constipation, concussion, seizure, and headache. We evaluated CT use by hospital type (pediatric academic, adult academic, community with and without pediatric consultation) and provider specialty [pediatric emergency medicine (PEM), emergency medicine (EM), family medicine + EM, family medicine, pediatrician], using multivariable logistic regression, adjusting for patient, ED, and physician characteristics. RESULTS: We included 599,948 pediatric ED discharges [mean (SD) age 10.8 y (5.3); 55.4% females]: 5000 (1.2%) discharges for abdominal diagnoses included a CT, and 21,398 (11.4%) discharges for neurological diagnoses included a CT. Children had an increased adjusted odds ratio [aOR (95% CI)] of receiving a CT at all hospital types compared with pediatric academic hospitals: adult academic hospitals ranging from 1.10 (1.01 to 1.21) for headache to 3.46 (1.89 to 6.36) for constipation, community hospitals with pediatric consultation ranging from 1.54 (1.45 to 1.63) for concussion to 3.74 (2.38-5.90) for constipation, and community hospitals without pediatric consultation ranging from 1.24 (1.15 to 1.33) for concussion to 2.29 (1.36 to 3.87) for constipation. Those patients seen by nonpediatric providers (EM, family medicine + EM, family medicine) were more likely to receive CT scans than PEM physicians for all diagnoses. CONCLUSIONS: Low-value CT use was higher among children treated in nonpediatric EDs and by nonpediatric providers. Improvement initiatives should target specific hospital types and specialties.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".