Practice Variation in Urine Collection Among Emergency Department Providers in Pre–toilet-trained Children With Suspected Urinary Tract Infection
Bibliographic record
Abstract
BACKGROUND: Urinary tract infections are a common cause of acute illness among children presenting to the emergency department (ED). Many techniques exist to collect urine specimens in pre-toilet-trained children. There is wide practice variation regarding the most appropriate collection method. This variation also appears to exist across national health organizations and societies. To date, little is known about the extent of practice variation in urine collection methods or the influence of patient and health care provider characteristics on the choice of collection method. MATERIALS AND METHODS: A cross-sectional survey was designed and comprised of 3 sections: pediatric emergency medicine physician demographics, pediatric ED demographics, and case scenarios designed to assess the context surrounding urine collection method choice. The survey was disseminated to pediatric emergency medicine physicians across Canada from February 2023 to April 2023. A descriptive analysis of the characteristics of pediatric emergency medicine physicians and the EDs in which they worked was performed. Multivariate logistic regression models were used to examine pediatric emergency medicine physicians and ED factors that influence urine collection methods. RESULTS: Of 235 surveys, 96 were returned (41% participation rate). Most respondents were aged 40 to 49 (n=31, 35.6%), female (60.5%), completed residency in Ontario (18.4%) and Quebec (17.2%), and worked at the Children's Hospital of Eastern Ontario (16%). There was variation in urine collection methods among pediatric emergency medicine physicians with a preference for transurethral catheterization and bladder stimulation versus other methods. Factors such as the length of wait time of patients to be seen in the ED (odds ratio=3.03, 95% CI=1.14-8.09) and year postmedical school (odds ratio=1.67, 95% CI=1.07-2.60) were associated with increased choice of urinary catheterization when selecting a urine collection method. CONCLUSIONS: The data suggests there is practice variation in urine collection methods among Canadian pediatric emergency medicine physicians. This practice variation is influenced by both individual providers and the demographics of EDs.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".