Cranberry Juice for the Prevention of Pediatric Urinary Tract Infection: A Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: Proanthocyanidins found in cranberry have been reported to have in vitro and in vivo antibacterial activity. We determined the effectiveness of cranberry juice for the prevention of urinary tract infections in children. MATERIALS AND METHODS: A total of 40 children were randomized to receive daily cranberry juice with high concentrations of proanthocyanidin vs cranberry juice with no proanthocyanidin for a 1-year period. The study was powered to detect a 30% decrease in the rate of symptomatic urinary tract infection with type I and II errors of 0.05 and 0.2, respectively. Toilet trained children up to age 18 years were eligible if they had at least 2 culture documented nonfebrile urinary tract infections in the calendar year before enrollment. Patients with anatomical abnormalities (except for primary vesicoureteral reflux) were excluded from study. Subjects were followed for 12 months. The participants, clinicians, outcome assessor and statistician were all blinded to treatment allocation. RESULTS: Of the children 39 girls and 1 boy were recruited. Mean and median patient age was 9.5 and 7 years, respectively (range 5 to 18). There were 20 patients with comparable baseline characteristics randomized to each group. After 12 months of followup the average incidence of urinary tract infection in the treatment group was 0.4 per patient per year and 1.15 in the placebo group (p = 0.045), representing a 65% reduction in the risk of urinary tract infection. CONCLUSIONS: Cranberry juice with high concentrations of proanthocyanidin appears to be effective in the prevention of pediatric nonfebrile urinary tract infections. Further studies are required to determine the cost-effectiveness of this approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".