Impact of 5α‐reductase inhibitor and α‐blocker therapy for benign prostatic hyperplasia on prostate cancer incidence and mortality
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of 5α-reductase inhibitors (5ARIs) and α-blockers among men with benign prostatic hyperplasia (BPH) in relation to prostate cancer (PCa) incidence, severity and mortality. PATIENTS AND METHODS: A retrospective 20-year cohort study in men residing in Saskatchewan, aged 40-89 years, with a BPH-coded medical claim between 1995 and 2014, was conducted. Cox proportional hazards regression was used to compare incidence of PCa diagnosis, metastatic PCa, Gleason score 8-10 PCa, and PCa mortality among 5ARI users (n = 4 571), α-blocker users (n = 7 764) and non-users (n = 11 677). RESULTS: In comparison with both non-users and α-blocker users, 5ARI users had a ~40% lower risk of a PCa diagnosis (11.0% and 11.4% vs 5.8%, respectively), and α-blocker users had an 11% lower risk of a PCa diagnosis compared with non-users. Overall, the incidence of metastatic PCa and PCa mortality was not significantly different among 5ARI or α-blocker users compared with non-users (adjusted hazard ratios [HR] of metastatic PCa: 1.12 and 1.13, respectively, and PCa mortality: 1.11 and 1.18, respectively, P > 0.05 for both drugs), but both 5ARI and a-blocker users had ~30% higher risk of Gleason score 8-10 cancer, adjusted HR 1.37, 95% confidence interval [CI] 1.03-1.82, P = 0.03, and adjusted HR 1.28, 95% CI 1.03-1.59, P = 0.02, respectively compared with non-users. CONCLUSION: The use of 5ARIs was associated with lower risk of PCa diagnosis, regardless of comparison group. Risk of high grade PCa was higher among both 5ARI users and α-blocker users compared with non-users; however, this did not translate into higher risk of PCa mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".