Characteristics of finasteride users in comparison with nonusers: A Nordic nationwide study based on individual‐level data from Denmark, Finland, and Sweden
Bibliographic record
Abstract
PURPOSE: Published epidemiological studies on the association between finasteride use and the risk of male breast cancer have been inconclusive due to methodological limitations including a few male breast cancer cases included. Determinants of male breast cancer have been studied, but it remains unexplored whether these are also related to finasteride use and thereby constitute potential confounders. This study aimed to assess whether there are differences between finasteride users and nonusers with regard to numerous potential confounders. METHODS: In total, 246 508 finasteride users (≥35 years) were identified in the prescription registries of Denmark (1995-2014), Finland (1997-2013), and Sweden (2005-2014). An equal number of nonusers were sampled. The directed acyclic graph (DAG) methodology was used to identify potential confounders for the association between finasteride and male breast cancer. A logistic regression model compared finasteride users and nonusers with regard to potential confounders that were measurable in registries and population surveys. RESULTS: Finasteride users had higher odds of testicular abnormalities (odds ratio [OR] 1.40; 95% confidence interval [CI] 1.36-1.44), obesity (1.31; 1.23-1.39), exogenous testosterone (1.61; 1.48-1.74), radiation exposure (1.22; 1.18-1.27), and diabetes (1.07; 1.04-1.10) and lower odds of occupational exposure in perfume industry or in high temperature environments (0.93; 0.87-0.99), living alone (0.89; 0.88-0.91), living in urban/suburban areas (0.97; 0.95-0.99), and physical inactivity (0.70; 0.50-0.99) compared to nonusers. CONCLUSIONS: Systematic differences between finasteride users and nonusers were found emphasizing the importance of confounder adjustment of associations between finasteride and male breast cancer.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".