Comparative effectiveness of antibiotics for uncomplicated urinary tract infections: Network meta-analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND: The efficacies and adverse effects of different antibiotics for uncomplicated urinary tract infections (UTIs) have been studied by standard meta-analytic methods using pairwise direct comparisons of antimicrobial treatments: the effects of one treatment are compared to those of either another treatment or placebo. However, for clinical decisions, we need to know the effectiveness of each possible treatment in comparison with all relevant alternatives, not with just one. OBJECTIVES: To compare the efficacies and adverse effects of all relevant antibiotics for UTI treatment simultaneously by performing a network meta-analysis using direct and indirect treatment comparisons. METHODS: Using logistic regression analysis, we performed a network meta-analysis of randomized controlled trials (RCTs) published after 1999 that compared different oral antibiotic or placebo regimens for UTI treatment in general practice or outpatient settings. We looked at five binary outcomes: early clinical, early bacteriological, late clinical and late bacteriological outcomes, as well as adverse effects. Consequently, a ranking of the antibiotic regimens could be composed. RESULTS: Using a network structure, we could compare and rank nine treatments from 10 studies. Overall, ciprofloxacin and gatifloxacin appeared the most effective treatments, and amoxicillin-clavulanate appeared the least effective treatment. In terms of adverse effects, there were no significant differences. DISCUSSION: Network meta-analysis shows some clear efficacy differences between different antibiotic treatments for UTI in women. It provides a useful tool for clinical decision making in everyday practice. Moreover, the method can be used for meta-analyses of RCTs across primary care and beyond.
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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 | 0.079 | 0.157 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".