Statin Use and Prostate Cancer Incidence in Manitoba, Canada: A Population-Based Nested Case–Control Study
Bibliographic record
Abstract
BACKGROUND: A link between statin use and prostate cancer risk has been proposed. Epidemiologic evidence is, however, inconclusive, and data for specific statin types as well as for period, duration, and dose of use are lacking. METHODS: We conducted a population-based nested case- control study using administrative data in Manitoba, Canada. Prostate cancer cases were matched to cancer-free controls, and their statin use (including period, duration, and dose of use) was assessed (with adjustment for prostate cancer screening) for statins as a class and for each specific statin. RESULTS: We matched 9,384 prostate cancer cases to 46,749 cancer-free controls. Ever use of any statin was not associated with prostate cancer risk, odds ratio (OR) 0.96 (95% confidence interval, 0.90-1.03). Except for pravastatin, 0.82 (0.71-0.96), individual statins were not associated with prostate cancer risk. There was no dose or duration response for pravastatin (or any other statin). CONCLUSIONS: We found limited evidence of an association between statin use and prostate cancer risk. The association between pravastatin and prostate cancer risk may be due to chance. IMPACT: We show that statin use is not associated with prostate cancer risk after adjustment for screening for a large population with data going back to the mid-1990s.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".