5α-Reductase Inhibitors and Risk of Kidney and Bladder Cancers in Men with Benign Prostatic Hyperplasia: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Preclinical evidence suggests that 5α-reductase inhibitors (5ARi), commonly used to treat benign prostatic hyperplasia (BPH), are associated with reduced incidence of certain urologic cancers, yet epidemiologic studies are conflicting. This study aimed to determine whether 5ARi's are associated with a reduced risk of kidney and bladder cancers. METHODS: We conducted a new-user active-comparator cohort study in the United Kingdom Clinical Practice Research Datalink. From a base cohort of patients with incident BPH, new users of 5ARi's and α-blockers were identified. Patients were followed up until a first ever diagnosis of kidney or bladder cancer, death from any cause, end of registration, or December 31, 2017. Cox proportional hazards models were used to calculate HRs and 95% confidence intervals (CI) for incident kidney and bladder cancer. RESULTS: There were 5,414 and 37,681 new users of 5ARi's and α-blockers, respectively. During a mean follow-up of 6.3 years, we found no association between the use of 5ARi's and kidney (adjusted HR, 1.26; 95% CI, 0.74-2.12; n = 23) or bladder (adjusted HR, 0.89; 95% CI, 0.64-1.23; n = 57) cancer risk compared with α-blockers. Similar results were observed across sensitivity analyses. CONCLUSIONS: In this study, we found no association between the use of 5ARi's and kidney or bladder cancer incidence in men with BPH when compared with α-blocker use. IMPACT: The findings of this study indicate that 5ARi's are unlikely to reduce kidney or bladder cancer risk.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".