Development and Validation of a Bovine Castration Model and Rubric
Bibliographic record
Abstract
Veterinary students require deliberate practice to reach competence in surgical bovine castration, but animal availability limits opportunities for practice. We sought to create and validate a surgical bovine castration model consisting of a molded silicone scrotum and testicles to allow students to practice this skill without the use of live animals. We sought to validate the model and associated scoring rubric for use in a veterinary clinical skills course. A convenience sample of third-year veterinary students ( n = 19) who had never castrated a bovine were randomized into two groups. The traditionally trained (T) group performed castration on a live bull calf after a 50-minute instructional lecture. The model-trained (M) group received the same lecture and a 2-hour clinical skills session practicing bovine castration using the model. All students were subsequently digitally recorded while castrating a live bull calf. Performance recordings were scored by an investigator blinded to group. Survey data were collected from the students and from expert veterinarians testing the model ( n = 8). Feedback from both groups was positive. The M group had higher performance scores than the T group (M group, M = 80.6; T group, M = 68.2; p = .005). Reliability of rubric scores was adequate at .74. No difference was found in surgical time (M group, M = 4.5 min; T group, M = 5.5 min; p = .12). Survey feedback indicated that experts and students considered the model useful. Model training improved students’ performance scores and provided evidence for validation of the model and rubric.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".