Finasteride Use and Risk of Male Breast Cancer: A Case–Control Study Using Individual-Level Registry Data from Denmark, Finland, and Sweden
Bibliographic record
Abstract
BACKGROUND: In case reports, concerns have been raised as to whether finasteride use increases the risk of male breast cancer. Previous epidemiologic evidence on the potential link is conflicting. This study aimed to assess whether an association between finasteride use and male breast cancer exists after accounting for potential confounders. METHODS: The source population consisted of all men (≥35 years) from Denmark (1995-2014), Finland (1997-2013), and Sweden (2005-2014). Cases with incident male breast cancer were identified in the cancer registries and matched with 50 density-sampled, age, and country-matched male population controls per case. Exposure information on finasteride use was derived from the prescription registries. Potential confounders were identified using the directed acyclic graph methodology and measured by use of information from nation-wide registries. RESULTS: The study population comprised 1,005 male breast cancer cases and 43,058 controls. Confounder-adjusted odds of finasteride exposure were not statistically significantly increased [OR, 1.09; 95% confidence interval (CI), 0.77-1.54] in breast cancer cases relative to controls. There was no evidence of a dose-response relationship, as the group with greatest exposure to finasteride was associated with lowest OR of male breast cancer [OR, 0.72 (95% CI, 0.40-1.30)]. Sensitivity analyses did not reveal marked changes in results with different exposure definitions or for specific subgroups. CONCLUSIONS: Results from this study provided no evidence that finasteride use was associated with male breast cancer. IMPACT: This large confounder-adjusted study supports the view that exposure to finasteride is not associated materially with male breast 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".