Use of nonsteroidal anti‐inflammatory drugs and prostate cancer risk: A meta‐analysis
Bibliographic record
Abstract
The association between use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) and the risk of prostate cancer remains controversial despite many observational epidemiological studies. We conducted a systematic meta-analysis of these studies to examine both the strength and the consistency of the association, and to explore sources of variability between studies. We searched 12 computerized literature databases for reports published before June 2008 and included any epidemiologic studies where the outcome was prostate cancer incidence or mortality, and the exposure was use of NSAIDs. Studies that met the inclusion criteria comprised 10 case-control and 14 cohort studies with a total of 24,230 prostate cancer cases. Studies that assessed the effect of aspirin use on total prostate cancer had a pooled odds ratio (POR) of 0.83 (95%CI: 0.77-0.89), whereas those that assessed the effect of aspirin on advanced prostate cancer had a POR of 0.81 (0.72-0.92). Studies that examined the effects of non-aspirin NSAIDs or all NSAIDs were less consistent but still suggestive of reduced risks. However, most reviewed studies were limited by exposure and disease misclassification, by inadequate information on dose and duration of use and by the possibility of screening and other biases. In conclusion, the epidemiologic evidence for a protective effect of aspirin and other NSAID use against prostate cancer is suggestive but not conclusive. There is a need for well-designed observational studies with adequate exposure measurements, accurate case definition, attention to latency effects, and careful adjustment for screening and other biases.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.052 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".