Predictors of success after bilateral epididymovasostomy performed during vasectomy reversal: A multi-institutional analysis
Bibliographic record
Abstract
INTRODUCTION: Vasectomy reversal (VR) represents an excellent option for paternity in men who desire to expand their family following vasectomy. Traditional VR via vasovasostomy has a success rate upwards of 90% but when sperm or sperm parts are not present in vasal fluid, epididymovasostomy (EV) must be performed instead. Our objective was to determine which factors influence success after bilateral EV. METHODS: A prospectively maintained database with data from the U.S. and Canada was used to identify men who underwent bilateral EV at time of VR. Success was defined as motile sperm in any postoperative semen analyses. Multivariable logistic regression was used to identify predictors of success. RESULTS: A total of 200 men had at least one postoperative semen analysis, and 171 men were included in the analysis. Average age was 44.7 years, with average followup of seven months. Median time elapsed between vasectomy and EV was 15 years (interquartile range [IQR] 10-18). Overall success rate was 50%. Despite the study being adequately powered, factors such as years since vasectomy (odds ratio [OR] 1.01, confidence interval [CI] 0.95-1.06), age (OR 0.96, CI 0.91-1.01), intraoperative presence of motile sperm (OR 0.81, CI 0.41-1.62), and epidydimal fluid characteristics did not predict success. CONCLUSIONS: Bilateral EV at time of VR is successful in 50% of cases in a multi-institutional, North American cohort. Microsurgeons can be reassured that neither time elapsed nor epididymal fluid characteristics negatively impact success rates as long as sperm or sperm parts are present. Surgeons performing VR should be comfortable and prepared to perform EV if indicated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".