124 Development and implementation of a patient decision aid on post-vasectomy semen analysis
Bibliographic record
Abstract
Introduction A semen analysis is recommended three months after vasectomy to assess the success of the procedure. However, typically in North America, only 55–71% of vasectomized men comply with the prescribed test. According to the Ottawa Decision Support Model (ODSM), deciding whether to do a post- vasectomy semen analysis (PVSA) or not is a ’difficult’ decision. Our study aimed to create a patient decision aid (PtDA) and implement it in the practice of Vasectomie Québec, Canada’s most important vasectomy provider. Methodology The development of the PtDA was based on the ODSM framework and the International Patient Decision Aid Standards (IPDAS) criteria, using the literature on the determinants of compliance to PVSA and Vasectomie Québec statistics on the effectiveness of vasectomy. Results The PtDA provides information on PVSA, vasectomy success/failure probabilities (including a pictogram), reasons for choosing to do the test or not, and resources to facilitate an informed decision. It was tested with 57 vasectomized men at Vasectomie Québec. Men regarded the PtDA as informative (95%) and helpful in understanding the merits and drawbacks associated with each option (98%). It facilitated clarification of their priorities (93%), the certainty of their decision (89%), and their deliberation (90%). Respondents expressed satisfaction with the tool, citing its user-friendliness (90%), clear presentation (98%), and appropriate font length (81%) and size (94%). Most reported that the language (98%) and statistical information (96%) were clear and easy to understand. Between October 2022 and September 2023, Vasectomie Québec supplied the final PtDA to over 4,700 vasectomized men. Conclusion A PtDA on PVSA was successfully developed and implemented in a large vasectomy clinic in Canada. It could be adapted to other settings according to their success/failure statistics and access to PVSA. An evaluation of the impact of the PtDA on decision-making and compliance is underway.
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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 | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".