The Effect of Statins on the Incidence and Prognosis of Bladder Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Statins are widely used due to their ability to lower plasma cholesterol and offer protection from the effects of atherosclerosis. However, their role in urology and specifically bladder cancer remains unclear. We aimed to systematically address this issue in the literature and determine any possible effects of statin therapy on bladder cancer. Methods: We searched MEDLINE (PubMed) and Cochrane Library databases for records up to 26 March 2023, for studies evaluating the effects of statins on urinary bladder cancer (UBC). We included all randomized controlled trials (RCTs), cohorts, and case-control studies that were conducted on the adult population. PROSPERO registration number: CRD42023407795. Results: Database searches returned 2251 reports, and after thorough investigation and assessment for eligibility, 32 reports were included in the analysis. Of them, 4 were RCTs, 6 were case-control studies, and 22 were cohort studies. Our qualitative analysis demonstrated no association between statin administration and UBC local control, recurrence, survival, or mortality, or between statin administration and bacille Calmette–Guérin (BCG) immunotherapy effectiveness. A meta-analysis of 10 trials revealed a non-significant reduction of 11% in UBC risk among users compared with non-users in RCTs (RR: 0.89, 95% CI 0.68–1.16, p = 0.37) and a non-significant increase of 32% of UBC risk among statin users compared with non-users in the analysis of the cohort studies (RR: 1.32, 95% CI 0.76–2.30, p = 0.33). Conclusions: Our results provide strong evidence to support the neutral effect of statins on UBC local control, recurrence, survival, and mortality, and on BCG immunotherapy. Our meta-analysis revealed a non-significant effect on UBC risk among statin users when compared with non-users, indicating no statin effect on UBC incidence and overall prognosis.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".