5-alpha Reductase inhibitors and risk of male breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objective: To assess the relationship between 5α-reductase inhibitors (5ARIs) and the risk of male breast cancer (MBC). Material and Methods: We systematically searched Medline via PubMed, Embase and the Cochrane Library Central Register up to May 2017 to identify published articles related to 5ARIs and the risk of MBC. Results: Summary effect estimates were calculated by a random-effect model, and tests for multivariable-unadjusted pooled risk ratios (RR) and heterogeneity, as well as the sensitivity analyses were conducted to assess publication bias. All four studies were conducted in a quality assessment according to the Newcastle Ottawa Scale system. The strength of association between 5ARIs and the prevalence of MBC was evaluated by using summarized unadjusted pooled RR with a 95% confidence interval [CI]. Four studies involving 595.776 participants, mean age range from 60 to 73.2 years old, were included in a meta-analysis, which produced a summary unadjusted RR of the risk of MBC for the treatment of 5ARIs of 1.16 (95% CI 0.85-1.58, P=0.36) and the multivariable-adjusted RR is 1.03, (95% CI 0.75-1.41, p=0.86). There was no heterogeneity among included studies (I2=0%, P=0.49). Estimates of total effects were generally consistent with the sensitivity. Conclusion: We did not observe a positive association between the use of 5ARIs and MBC. The small number of breast cancer cases exposed to 5ARIs and the lack of an association in our study suggest that the development of breast cancer should not influence the prescribing of 5ARIs therapy.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".