Factors predicting overall success: a review of 747 microsurgical vasovasostomies
Bibliographic record
Abstract
OBJECTIVES: Advances in surgical techniques have improved the outcome of microsurgical vasovasostomy (VV). We performed a retrospective analysis of surgical procedures to determine outcomes and predictors of VV success, to develop Kaplan-Meier Curves for predicting VV outcomes and to evaluate the use of alpha-glucosidase (AG) to predict outcomes. PATIENTS AND METHODS: We undertook a retrospective analysis of 747 modified 1-layer microsurgical VV procedures performed between 1984 and 2000. Obstructive interval, partner status, social status preoperatively and method of vasal obstruction, vasal fluid quality and sperm granuloma intraoperatively were compared with outcome results. Parameters evaluated at follow-up included semen analysis, AG concentration in ejaculate fluid and pregnancy rates. RESULTS: The overall patency rate was 86% and pregnancy rates were 33% and 53% at 1 and 2 years after primary VV, respectively. Preoperative factors associated with successful outcome and pregnancy included shorter obstructive interval and same female partner (p < 0.05). Intraoperative factors predicting success included the use of surgical clips instead of suture at vasectomy, the presence of a sperm granuloma, the presence and quality of vasal fluid, and the presence and quality of sperm in vasal fluid. Further, increased AG in the postoperative semen predicted improved patency and pregnancy outcomes. CONCLUSION: This study confirms the effectiveness of VV for vasectomized men who wish to father children. It also demonstrates that preoperative and intraoperative factors are predictive of the VV outcome. Postoperative AG is also a useful marker of patency and it appears to predict pregnancy outcome.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".