Vasectomy and risk of prostate cancer: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
The results of published literature focusing on the association between vasectomy and the incidence of prostate cancer are often inconsistent. We conducted a meta-analysis to provide a quantitative assessment of the association between vasectomy and the risk of prostate cancer. We identified all cohort studies by searching the PubMed, Embase, and Cochrane Library before August 2014. The quality of the studies was evaluated using the Newcastle-Ottawa Scale checklist. Summary effect estimates with 95% confidence intervals (CI) were derived using a fixed or random effects model, depending on the heterogeneity of the included studies. Nine cohort studies that spanned across two continents involving 1 127 096 participants (ages 20-75) with 7539 cases of prostate cancer cases were included in the meta-analysis. The overall combined relative risks for men with the reference group were 1.08 (95% CI: 0.87-1.34) in a random effects, however, the association was not statistically significant (p = 0.48). Estimates of total effects were generally consistent in the sensitivity and subgroup analyses. No evidence of publication bias was observed. This meta-analysis indicated that vasectomy may not contribute to the risk of prostate cancer. The conclusion might have a far-reaching significance for the public health, especially in countries with high prevalence rates of vasectomy.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".