Frequency and patterns of early recanalization after vasectomy
Bibliographic record
Abstract
BACKGROUND: Our understanding of early post-vasectomy recanalization is limited to histopathological studies. The objective of this study was to estimate the frequency and to describe semen analysis patterns of early recanalization after vasectomy. METHODS: Charts displaying serial post-vasectomy semen analyses were created using the semen analysis results from 826 and 389 men participating in a randomized trial of fascial interposition (FI) and an observational study of cautery, respectively. In the FI trial, participants were randomly allocated to vas occlusion by ligation and excision with or without FI. In the cautery study, sites used their usual cautery occlusion technique, two with and two without FI. Presumed early recanalization was based on the assessment of individual semen analysis charts by three independent reviewers. Discrepancies were resolved by consensus. RESULTS: Presumed early recanalization was characterized by a very low sperm concentration within two weeks after vasectomy followed by return to large numbers of sperm over the next few weeks. The overall proportion of men with presumed early recanalization was 13% (95% CI 12%-15%). The risk was highest with ligation and excision without FI (25%) and lowest for thermal cautery with FI (0%). The highest proportion of presumed early recanalization was observed among men classified as vasectomy failures. CONCLUSION: Early recanalization, occurring within the first weeks after vasectomy, is more common than generally recognized. Its frequency depends on the occlusion technique performed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".