Prostate cancer and use of nonsteroidal anti-inflammatory drugs: systematic review and meta-analysis
Bibliographic record
Abstract
Animal and laboratory studies suggest that regular use of nonsteroidal anti-inflammatory drugs (NSAIDs) may reduce prostate cancer risk. To assess this association, we conducted a systematic review and meta-analysis of observational studies published before January 2003. We derived summary odds ratios (ORs) using both fixed and random effects models and performed subgroup analyses to explore the possible sources of heterogeneity between combined studies. We identified 12 reports (five retrospective and seven prospective studies). Most studies of aspirin use reported inverse associations, but only two were statistically significant. The summary OR for the association between aspirin use and prostate cancer was 0.9 (95% confidence interval: 0.82-0.99; test of homogeneity P=0.32), and varied from 1.0 for retrospective studies to 0.85 for prospective studies. Studies that measured exposure to a mixture of NSAIDs were less consistent. These results indicate an inverse association between aspirin use and prostate cancer risk. The current epidemiological evidence and, in particular, the strong and consistent laboratory evidence underline the need for additional epidemiological studies to confirm the direction and magnitude of the association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".