Nonintravesical Interventions for Preventing Intravesical Recurrence in Patients With Nonmuscle-Invasive Bladder Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Despite currently used intravesical therapies in non-muscle-invasive bladder cancer (NMIBC), the rate of intravesical recurrence remains very high. We aimed to evaluate the effectiveness of adding nonintravesical interventions to standard intravesical therapies to prevent intravesical recurrence. In April 2024, 3 databases were queried for prospective studies evaluating nonintravesical interventions in addition to standard intravesical therapies for NMIBC (CRD42024490988). The primary outcome was intravesical recurrence-free survival (iRFS). Standard pairwise meta-analyses were performed using hazard ratios (HR) and 95% confidence intervals (95% CI) with a random-effects model. We identified 18 eligible studies (14 RCTs and 4 prospective trials) comprising 4,593 NMIBC patients, which investigated pharmacological interventions (eg, selenium, vitamins, Lactobacillus casei, celecoxib, metformin, mistletoe lectin) and lifestyle modifications (diet). The addition of Lactobacillus casei significantly improved iRFS (HR: 0.50; 95% CI: 0.34-0.73; P < .001). A high western diet pattern significantly worsened iRFS (HR:1.48, 95%CI:1.06-2.06, P = .03). The other nonintravesical interventions were not associated with iRFS. Our comprehensive review of the published literature highlights the need for further research into the efficacy of nonvesical interventions for NMIBC. While Lactobacillus was shown to improve iRFS in 2 RCTs, additional high-quality randomized studies are required to evaluate the effectiveness of other interventions.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| 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) | 0.004 | 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".