Impact of antibiotic prophylaxis on urinary tract infection recurrence in children
Bibliographic record
Abstract
INTRODUCTION: Given the potential consequences associated with urinary tract infections (UTIs), it has become standard practice to use continuous antibiotic prophylaxis (CAP) in children, even if controversial. We reviewed the effectiveness of CAP on recurrent UTI in a pediatric population to determine if equipoise remains and allows for a placebo control group to study the effectiveness of the vaccine MV140. METHODS: We completed a rapid review. We searched Medline, Embase and the Cochrane Library and data extraction was completed by a single reviewer. Our search criteria were 2005-2022, English and French language, randomized controlled trials (RCTs) and systematic reviews only. The population was 19 years and younger, including: vesicoureteral reflux (VUR), congenital anomalies of the kidneys and urinary tracts (CAKUT), and bladder and bowel dysfunction (BBD). RESULTS: Three RCTs and three systematic reviews found a benefit for CAP, mostly for a population with VUR, and those with severe VUR have more benefit. Most studies were not able to show a difference in the rate of UTIs or new renal scars (NRS). Three RCTs found a deleterious effect with CAP. Other studies were able to prove a benefit for patients with dilatation of the urinary tract without obstruction and high-grade VUR combined with BBD. The major adverse event found was antimicrobial resistance. CONCLUSIONS: High-risk patients benefit from CAP. The potential consequences of UTIs makes it unethical to use a placebo-only control group for them; however, CAP use seems difficult to justify in a low-risk population.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".