Techniques – Tension-relieving microdot vasovasostomies and longitudinal intussuscepted vasoepididymostomy vasectomy reversals
Bibliographic record
Abstract
INTRODUCTION: Tension and malalignment of vasectomy reversal (VR) anastomoses are hypothesized to contribute to failure. We report VR outcomes using a novel technique introducing a tension-reliving hitch in the multilayer microdot vasovasostomy (VV) and longitudinal intussuscepted vasoepididymostomy (LIVE; VE). METHODS: All VR patients between May 2019 and September 2023 from a single surgeon were reviewed. Patients were included if they underwent a VR with at least one semen analysis within six months of surgery and a minimum of six months of followup after the surgery to deem a failure. The primary outcome was patency, which was defined as 1) any sperm in the ejaculate; and 2) functionally as at least two million motile sperm. Late failure was defined as an azoospermic semen analysis result after previously documented presence of sperm. RESULTS: A total of 159 patients were evaluated, of which 136 patients met the inclusion criteria. The patency rate among all VRs was 97.7 %, with an overall functional patency rate of 93.1%. One hundred and one patients underwent bilateral VVs, with a 99% patency rate and 95.5% functional patency rate. Twenty-three patients underwent a mixed VV/VE, with a patency rate of 100% and a functional patency rate of 88.8%. Finally, 12 patients underwent bilateral VE, with a patency rate of 83.3% and a functional patency rate of 77.7%. Among these patients, four VV patients were identified to have a late failure. CONCLUSIONS: The combination of tension-relieving stitches for VVs and VEs, along with attention to symmetrical and precise stitch placement, results in high patency rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".