The effect of statin use on the incidence of prostate cancer: A population‐based nested case–control study
Bibliographic record
Abstract
Preclinical studies suggest statins may help prevent prostate cancer (PC), but epidemiologic results are mixed. Many epidemiological studies have relatively short prediagnosis drug exposure data, which may miss some statin use. We completed a nested case-control study investigating the impact of statin use on PC diagnosis and clinically significant PC using data from men aged ≥40 years in the Canadian province of Saskatchewan between 1990 and 2010. Drug exposure histories were derived from a population-based prescription drug database. We used conditional logistic regression to model use of statins as a class and stratified analyses for groups defined by lipophilicity. Clinically significant PC was defined as Gleason score 8-10 OR stage C or D or III or IV at diagnosis. 12,745 cases of PC were risk-set matched on age and geographic location to 50,979 controls. Greater than 90% of subjects had prediagnosis drug exposure histories >15 years. 2,064 (16.2%) cases and 7,956 (15.6%) controls were dispensed one or more statin prescriptions. In multivariable models, ever prescription of statins was not associated with PC diagnosis (OR 0.97; 95% CI 0.90-1.05). Neither lipophilic statins (OR 0.96, 95% CI 0.88-1.04) nor hydrophilic statins (OR 1.06, 95% CI 0.95-1.20) impacted PC diagnosis. There was no effect of the dose or duration of statin use. Diagnosis of clinically significant PC decreased with statin use (OR 0.84, 95% CI 0.73-0.97). Statin use is not associated with overall PC risk, regardless of duration or dose of statin exposure. Statin use is associated with a decreased risk of clinically significant PC.
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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".