International meta-analysis of 684,660 men with vasectomies: a study utilising the International Population Data Linkage Network
Bibliographic record
Abstract
IntroductionEvidence on the effect of vasectomy and vasectomy reversal on risk of prostate cancer is conflicting, with the issue of detection bias a key criticism. In this study we examined the effect of vasectomy reversal on prostate cancer risk in a cohort of vasectomised men. Objectives and ApproachA proof of concept study involving the International Population Data Linkage Network which pooled aggregated result data from participating centres in Australia, Canada and the United Kingdom. De-identified linked data extractions took place at each centre. Each participating centre locally conducted Cox proportional hazards regression analysis compared the risk of prostate cancer in those with/without vasectomy reversal in a cohort of vasectomised men. These results were then combined in a meta-analysis. Evidence of a protective effect of vasectomy reversal would suggest the harmful effect of vasectomy on prostate cancer risk, while nullifying detection bias. ResultsData were received from Australia (the states of Western Australia and New South Wales), Canada (the province of Ontario), Wales and Scotland. In total, there were 9,754 men with vasectomy reversals, and 684,660 men with a vasectomy. The combined analysis showed no protective effect of vasectomy reversal on incidence of prostate cancer when compared to those who had vasectomy alone (HR, 95%CI: 0.92, 0.70-1.21). As such, the results align with previous studies which found little or no evidence of a link between vasectomy and prostate cancer. Conclusion/ImplicationsThe study, originally conceived at the first IPDLN meeting in London, found no obvious protective effect of vasectomy reversal on prostate cancer in vasectomised men. The project demonstrated the utility and feasibility of collaborative studies fostered through the IPDLN, despite methodological challenges faced when aggregating international data.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".