Antibiotic Prescription Practice for Pediatric Urinary Tract Infection in a Tertiary Center
Bibliographic record
Abstract
OBJECTIVES: Prescribing antibiotics for suspected urinary tract infection (UTI) is common practice and may lead to unnecessary antibiotic exposure. We aimed to review UTI diagnosis and management in the emergency department and to identify targets for antimicrobial stewardship. METHODS: Single-center, retrospective cohort study of children aged 12 weeks to younger than 18 years discharged from the emergency department with a diagnosis of UTI between October and December 2016. Children with genitourinary malformations were excluded. Clinical information, urine collection method, laboratory findings, and urine culture results were gathered. The sensitivity and specificity of nitrite and leukocyte esterase for UTI diagnosis were calculated. The relationship between urinalysis characteristics and confirmed UTI was examined using logistic regression. RESULTS: A total of 183 children with a median (interquartile range) age of 4.2 (1.1-7.5) years were included; 82.5% were female. Almost all children were discharged home on antibiotics (n = 180, 98%) for a median (interquartile range) duration of 7 (7-10) days. A total of 85 patients (46.4%) received antibiotics despite negative urine cultures leading to 525 unnecessary antibiotic days. The presence of nitrites was the strongest predictor of UTI (odds ratio = 20.22, P < 0.001) and was highly specific. CONCLUSIONS: Current practice in managing suspected pediatric UTIs in our ED resulted in significant and unnecessary antibiotic exposure. We identified targets to reduce unnecessary antibiotic exposure including improving the diagnostic accuracy of UTIs, a process to discontinue antibiotics for negative cultures and standardizing antimicrobial duration.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".