Dosage, duration and timing of nonsteroidal antiinflammatory drug use and risk of prostate cancer
Bibliographic record
Abstract
Experimental studies suggest that NSAIDs could reduce prostate cancer risk. Results of observational studies on the relation between NSAIDs and prostate cancer risk have, however, been inconsistent. Moreover, none has addressed the issues of dosage, duration and timing of exposure. In a population-based, age-matched case-control study, we measured the association between prostate cancer risk and NSAIDs defined in terms of mean daily dose, cumulative duration and timing of exposure. Eight-year drug exposure history was obtained from the Quebec health insurance system database. Parallel analyses were performed for aspirin and NSAIDs other than aspirin. We controlled for detection bias and assessed the potential impact of protopathic bias. Analyses were performed with conditional logistic regression. Among the 2,221 cases and 11,105 controls, there was a negative trend between cumulative duration of aspirin use and prostate cancer risk (p = 0.0009). Also, exposure to a mean daily dose of aspirin of at least 80 mg, maintained throughout the entire 8 years of follow-up, was associated with an 18% reduction in prostate cancer risk (OR = 0.82, 95% CI 0.71-0.95). In more recent users of such a dose, the risk reduction was 7%. However, 1 year after the end of a 7-year regular aspirin exposure, no residual protective effect persisted. No association was observed between prostate cancer risk and exposure to NSAIDs other than aspirin. The results suggest that long-term and regular use of aspirin, at a dosage beneath that usually recommended for an anti-inflammatory effect, may prevent prostate cancer.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".