Risk of post-vasectomy infections in 133,044 vasectomies from four international vasectomy practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To estimate the risk of post-vasectomy infections in various settings and across various surgical techniques and sanitization practices. PATIENTS AND METHODS: Retrospective review of the records of 133,044 vasectomized patients from four large practices/network of practices using the no-scalpel vasectomy (NSV) technique in Canada (2011-2021), Colombia (2015-2020), New Zealand (2018-2021), and the United Kingdom (2006-2019). We defined infection as any mention in medical records of any antibiotics prescribed for a genital or urinary condition following vasectomy. RESULTS: Post-vasectomy infection risks were 0.8% (219 infections/26,809 procedures), 2.1% (390/18,490), 1.0% (100/10,506), and 1.3% (1,007/77,239) in Canada, Colombia, New Zealand, and the UK, respectively. Audit period comparison suggests a limited effect on the risk of infection of excising a short vas segment, applying topical antibiotic on scrotal opening, wearing a surgical mask in Canada, type of skin disinfectant, and use of non-sterile gloves in New Zealand. Risk of infection was lower in Colombia when mucosal cautery and fascial interposition [FI] were used for vas occlusion compared to ligation, excision, and FI (0.9% vs. 2.1%, p<0.00001). Low level of infection certainty in 56% to 60% of patients who received antibiotics indicates that the true risk might be overestimated. Lack of information in medical records and patients not consulting their vasectomy providers might have led to underestimation of the risk. CONCLUSION: Risk of infection after vasectomy is low, about 1%, among international high-volume vasectomy practices performing NSV and various occlusion techniques. Apart from vasectomy occlusion technique, no other factor modified the risk of post-vasectomy infection.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it