Lactobacillus for preventing recurrent urinary tract infections in women: meta-analysis.
Bibliographic record
Abstract
INTRODUCTION: Urinary tract infections (UTIs) are the most common infections affecting women, and often recur. Lactobacillus probiotics could potentially replace low dose, long term antibiotics as a safer prophylactic for recurrent UTI (rUTI). This systematic review and meta-analysis was performed to compile the results of existing randomized clinical trials (RCTs) to determine the efficacy of probiotic Lactobacillus species in preventing rUTI. MATERIALS AND METHODS: MEDLINE and EMBASE were searched from inception to July 2012 for RCTs using a Lactobacillus prophylactic against rUTI in premenopausal adult women. A random-effects model meta-analysis was performed using a pooled risk ratio, comparing incidence of rUTI in patients receiving Lactobacillus to control. RESULTS: Data from 294 patients across five studies were included. There was no statistically significant difference in the risk for rUTI in patients receiving Lactobacillus versus controls, as indicated by the pooled risk ratio of 0.85 (95% confidence interval of 0.58-1.25, p = 0.41). A sensitivity analysis was performed, excluding studies using ineffective strains and studies testing for safety. Data from 127 patients in two studies were included. A statistically significant decrease in rUTI was found in patients given Lactobacillus, denoted by the pooled risk ratio of 0.51 (95% confidence interval 0.26-0.99, p = 0.05) with no statistical heterogeneity (I2 = 0%). CONCLUSION: Probiotic strains of Lactobacillus are safe and effective in preventing rUTI in adult women. However, more RCTs are required before a definitive recommendation can be made since the patient population contributing data to this meta-analysis was small.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.053 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".