Nonsteroidal Antiinflammatory Drugs and Bladder Cancer: A Pooled Analysis
Bibliographic record
Abstract
Case-control studies have shown that regular use of nonsteroidal antiinflammatory drugs (NSAIDs) decreases bladder cancer risk, but few cohort studies have evaluated this association. The authors investigated NSAID use and bladder cancer in 3 large prospective studies (NIH-AARP Diet and Health Study; Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial; and U.S. Radiologic Technologists Study). Frequency of aspirin and nonaspirin NSAID use 1 year prior to baseline was ascertained using self-administered questionnaires. Study-specific hazard ratios and 95% confidence intervals were estimated using Cox regression models and were combined using a fixed-effects meta-analytic model. Data from all studies were aggregated, and aggregated hazard ratios were estimated. The analysis included 508,842 individuals, with 2,489 incident cases of bladder cancer. A reduction in risk was observed for individuals who reported regular use (>2 times/week) of nonaspirin NSAIDs compared with those who reported no use (hazard ratio (HR) = 0.92, 95% confidence interval (CI): 0.81, 1.04). The risk reduction was limited to nonsmokers (HR = 0.58, 95% CI: 0.41, 0.83) (P(trend) = 0.008) (P(interaction) = 0.02). No association was observed between regular aspirin use and bladder cancer risk (HR = 1.04, 95% CI: 0.94, 1.15). Results suggest that nonaspirin NSAIDs, but not aspirin, are associated with a reduction in risk of bladder cancer, particularly for nonsmokers.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.042 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".