Factors affecting post-vasectomy semen analysis compliance in home- and lab-based testing
Bibliographic record
Abstract
INTRODUCTION: We used a home-based (HB) post-vasectomy semen analysis (PVSA) between 2014 and 2017, but we have since reverted to local lab-based (LB) testing. In this study, we compared PVSA compliance rates in HB and LB test settings and describe factors that may influence completion rates. METHODS: We retrospectively identified patients who underwent vasectomy at our institution. Surgeons X and Y performed vasectomies from 2014-2017 using a HB immunochromatographic PVSA kit. From 2017-2020, surgeon X used a local LB PVSA. We collected data on PVSA completion status and patient demographics to perform two analyses. HB testing was examined by assessing all patients who had a vasectomy from 2014-2017. Another compared HB and LB testing by looking at surgeon X vasectomies from 2014-2017 and 2017-2020. RESULTS: We identified 285 patients who underwent vasectomy from 2014-2017 and were assessed with HB testing. Compliance with PVSA was 35% with HB PVSA. Age at vasectomy, number of children, and surgeon influenced PVSA completion in the 2014-2017 cohort. Surgeon X PVSA completion was 29% for the HB (n=136) testing cohort and 46% for the LB (n=201) cohort (odds ratio 0.47, 95% confidence interval 0.29-0.74). Again, more children decreased PVSA completion. CONCLUSIONS: Compliance with PVSA testing was inadequate in both test settings, although it was significantly higher in local LB setting. Based on these findings, the convenience of HB testing appears to decrease compliance with PVSA, although surgeon factors may be influential. These findings may help surgeons identify factors that improve PVSA compliance 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 | 0.007 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".