Effect of statin use on outcomes of non‐muscle‐invasive bladder cancer
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of statin use on outcomes of patients with non-muscle-invasive bladder cancer (NMIBC). To measure the effect of statin use on the efficacy of intravesical bacillus Calmette-Guérin (BCG) therapy. PATIENTS AND METHODS: A retrospective analysis was performed on 1117 patients treated with transurethral resection of the bladder (TURB) for NMIBC at three institutions between 1996 and 2007. Statin use at the time of diagnosis was recorded for each patient. Univariable Cox regression models addressed the association of statin use with disease recurrence, disease progression, cancer-specific mortality and overall mortality in all patients, patients with primary NMIBC, patients not treated with BCG, and patients treated with BCG. RESULTS: Overall, 341 patients (30.5%) used statins and 776 (69.5%) did not. Within a median (interquartile range) follow-up of 62.7 (25.0-110.7) months, 469 patients (42.0%) experienced disease recurrence, 103 (9.2%) progression, 50 (4.5%) cancer-specific mortality, and 299 (26.8%) any-cause mortality. In univariable Cox regression analyses, statin use was not associated with any of these four endpoints (P > 0.05 for all). In subgroup analyses, statin use was also not associated with prognosis in patients with primary NMIBC or patients not receiving BCG (P > 0.05 for all four endpoints). Statin use was not associated with response to BCG (P > 0.05 for all four endpoints). CONCLUSION: Statin users did not experience different outcomes compared with non-users and statin use did not affect the efficacy of BCG immunotherapy; these data do not support modification or discontinuation of statin therapy for patients with NMIBC.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".