Association between nonsteroidal anti-inflammatory drugs and prostate cancer occurrence.
Bibliographic record
Abstract
UNLABELLED: Prostate cancer is the most common malignancy among men in Western nations. Previous studies indicate that nonsteroidal anti-inflammatory drugs have an inhibitory effect on prostate cancer cells. We evaluated the association between frequent use of nonsteroidal anti-inflammatory drugs and prostate cancer occurrence. METHODS: We conducted a nested case-control study using medical administrative databases. All men older than 65 years of age who had filled at least one prescription for nonselective nonsteroidal anti-inflammatory drugs, cyclooxygenase-2 inhibitors (coxibs), aspirin, or acetaminophen between January 1999 and December 2002 were eligible. Among this group, we identified men who underwent prostate biopsy between January 2000 and June 2002 and did not have a diagnosis of any cancer in the preceding 2-year period. Cases were those with a diagnosis of prostate cancer. Controls were those who did not receive a diagnosis of any cancer. Logistic regression models were used to determine associations between prostate cancer occurrence and frequent exposure (more than 4 months) to nonsteroidal anti-inflammatory drugs/cyclooxygenase-2 inhibitors or aspirin during the prior 2 years in comparison with no exposure to any of these drugs, adjusting for age and prior finasteride use. RESULTS: We identified 2025 cases and 2150 controls. Older men were at greater risk for developing prostate cancer. Exposure to nonsteroidal anti-inflammatory drugs/cyclooxygenase-2 inhibitors was associated with a reduced likelihood of prostate cancer (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.58-0.86) as was exposure to aspirin (OR, 0.84; 95% CI, 0.74-0.96). DISCUSSION: Our results suggest that among men 65 years of age or older, frequent use of nonsteroidal anti-inflammatory drugs/cyclooxygenase-2 inhibitors and use of aspirin are associated with a reduced risk of prostate cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".