Variation in low-value radiograph use for children in the emergency department: a cross-sectional study of administrative databases
Bibliographic record
Abstract
BACKGROUND: Radiograph use contributes to low-value care for children in emergency departments (EDs), but little is known about systemic factors associated with their use. This study compares low-value radiograph use across ED settings by hospital type, pediatric volumes and physician specialty. METHODS: This is a cross-sectional study of routinely collected administrative data. We included children (age 0-18 yr) discharged from EDs in Ontario, Canada, between 2010 and 2019 with diagnoses of bronchiolitis, asthma, abdominal pain and constipation. Multiple clinical practice guidelines recommend against routine radiograph use in these conditions. Logistic regression evaluated odds of low-value radiograph by ED setting (pediatric academic [referent], adult academic, community with or without pediatric consultation services), pediatric volume and physician specialty (pediatric emergency medicine [PEM, referent], emergency medicine [EM], family medicine with EM training, pediatrics, family medicine), adjusting for demographic, clinical and provider characteristics. We used generalized estimating equations to account for clustering by ED. RESULTS: Of the total 9 862 787 eligible pediatric ED discharges in Ontario, 60 914 children had bronchiolitis, 141 921 asthma, 333 332 abdominal pain and 110 514 constipation; 26.0% received low-value radiographs. Compared with pediatric EDs and PEM physicians (referents), patients with bronchiolitis were most likely to have a chest radiograph in adult academic EDs (adjusted odds ratio [OR] 5.1 [95% confidence interval (CI) 4.6-5.6]) and by family physicians with EM training (adjusted OR 4.8 [95% CI 4.5-5.1]). Patients with asthma were more likely to have a chest radiograph in adult academic EDs (adjusted OR 3.0 [95% CI 2.8-3.2]) and by EM physicians (adjusted OR 2.8 [95% CI 2.6-3.0]). Patients with abdominal pain and constipation were more likely to have abdominal radiographs in community hospitals with pediatric consultation (adjusted OR 1.6 [95% CI 1.6-1.7] and 2.3 [95% CI 2.3-2.4], respectively) and by family physicians with EM training (adjusted OR 1.6 [95% CI 1.6-1.7] and 2.1 [95% CI 2.0-2.2], respectively). INTERPRETATION: Over the decade-long study period, low-value radiograph use was frequent for children with 4 common conditions seen in Ontario EDs. Quality improvement initiatives aimed at reducing unnecessary radiographs in children should focus on EM physicians practising in EDs that primarily treat adult patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".