Development and Evaluation of a Surgical Simulator and Assessment Rubric for Standing Castration of the Horse
Bibliographic record
Abstract
In veterinary education, simulators are models or devices that can imitate a real patient or scenario and allow students to practice skills without the need for live patients. Castration is a common surgical procedure in all species, and the standing, open technique is frequently performed in horses. Although a simulator has been developed for equine closed castration, a simulator for standing castration in the horse has not yet been described. This two-part study focused on the design, creation, and evaluation of a simulator for teaching standing castration in the horse. A low-technology simulator was created using molded silicone testicles, cohesive bandage, stockings, and socks. A rubric was created for assessing performance using the simulator. Participants were recruited from three groups: university academic staff members ( n = 12, majority equine veterinarians), equine veterinarians working in private practice ( n = 9), and final-year veterinary students ( n = 28). Each group tested the simulator while being graded using the developed rubric, and participants completed an anonymous online feedback questionnaire. Feedback was positive overall, with 98% of respondents ( n = 48/49) stating that the model would be a useful addition to the veterinary curriculum. Furthermore, 100% of students reported that using the simulator increased their confidence in performing standing castration in horses. Evaluation of the model included assessment of responses from veterinarians and students regarding realism and usefulness of the simulator, comparison of rubric scores between veterinarians and students, and assessment of the reliability of the rubric. Median student rubric score was significantly lower than qualified veterinarians ( p < .001), and Cronbach's alpha demonstrated that there was adequate internal reliability in rubric scoring (α = .85). It was determined that the simulator is effective for teaching the steps of the surgical procedure and for increasing student confidence.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".