Procedural simulators for teaching and learning vasectomy techniques: a scoping review
Bibliographic record
Abstract
Background: Procedural simulators can facilitate teaching and improve learning vasectomy surgical techniques. The objectives of this scoping review were to identify available vasectomy simulators (scrotal models), and to assess their characteristics and potential suitability for optimal transfer of surgical skills of most recommended techniques in clinical practice. Methods: We performed searches up to December 2023 using PubMed and Google search engines to identify existing vasectomy simulators. Articles and Web pages reporting vasectomy simulators were also examined using a snowball strategy. In addition, we asked members of the Vasectomy Network, an international Google discussion group, if they knew any other simulators. Two members of the research team performed the initial evaluations of the physical and functional characteristics of retrieved simulators. All team members made consensus on final evaluations. Results: in the USA and seven were homemade models. All had limited visual and haptic realism of internal and external structures. Most, however, were suitable for simulating some basic skills of the no-scalpel technique to deliver the vas deferens. Fascial interposition could not be simulated with any model. Commercially available models had no advantage over homemade models. Conclusions: Most vasectomy simulators currently available allow learning some basic surgical skills of the procedure but have limitations for optimal learning of the recommended techniques and skill transfer in clinical practice. There appears to be a need to develop and evaluate new simulators with enhance visual and haptic characteristics for teaching and learning vasectomy techniques.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".