Non-steroidal anti-inflammatory drug use and prostate cancer in a high-risk population
Bibliographic record
Abstract
Animal and laboratory studies suggest that regular use of non-steroidal anti-inflammatory drugs (NSAIDs) may reduce prostate cancer risk. The aim of this study was to investigate the association between NSAID use and prostate cancer in a high-risk population. We included 1299 men who were referred to our university's prostate cancer detection clinic for prostate biopsy between January 1999 and July 2003. Before transrectal ultrasonography and prostate biopsy, all men completed a self-administered questionnaire that included questions on drug use in the preceding 5 years. On average, NSAID users were older than non-users but there was no significant difference in mean baseline prostate-specific antigen (PSA). Four hundred and ninety-four (38%) had biopsy-confirmed prostate cancer. After adjustment for age, family history of prostate cancer and other potential confounders, use of aspirin was associated with a 42% reduction in the odds of prostate cancer detection [95% confidence interval (CI) 0.36-0.91]. Among cases, regular use of NSAIDs was inversely related to the risk of detection of more poorly differentiated cancers and cancers with higher percentage core involvement. These findings support other epidemiological and experimental evidence that suggests that aspirin may be useful in prostate cancer prevention. Further observational studies with adequate case definition and exposure measurements and careful adjustment for detection bias are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".