Children with recurrent urinary tract infections: who are they and why do we need better prevention options?
Bibliographic record
Abstract
BACKGROUND: Recurrent UTIs (rUTIs) in children can lead to renal scarring and chronic renal failure, if not managed early. The current standard of care involves antibiotic prophylaxis, which benefits remain controversial. There is a need for new interventions, other than antimicrobial use, to reduce the occurrence of rUTIs as well as the identification of groups to target. Our objective was to describe the pediatric population most susceptible to rUTIs, including renal scarring and adverse events associated with prolonged antibiotic use. METHOD: We conducted a single-centre retrospective chart review of patients with rUTIs, diagnosed with urinary tract abnormalities or functional disorders, followed at the Centre hospitalier universitaire (CHU) Sainte-Justine urology clinic between January 2015 and December 2020. We described the population and evaluated the antimicrobial usage and uropathogens resistance patterns. RESULTS: Identified patients with rUTI (n = 107) had underlying medical conditions such as Spina Bifida or neurogenic bladder (31%), double collecting system (22%), vesicoureteral reflux (42%) and ureteropelvic junction obstruction (7%). Almost all patients (87%) were prescribed antimicrobial prophylaxis, and a significant proportion developed resistance, with 70% of breakthrough UTIs being resistant to at least one drug, and 41% demonstrating multi-drug resistance. CONCLUSION: While most patients received prophylaxis, it was not universally effective, leading to persistent concerns like renal scarring and adverse events associated with prolonged antimicrobial use. There is a critical need for the development of new strategies to prevent rUTI that would minimize reliance on antibiotic prophylaxis, given the escalating global threat of antimicrobial resistance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".