Development and Validation of a Canine Castration Model and Rubric
Bibliographic record
Abstract
Veterinary educators use models to allow repetitive practice of surgical skills leading to clinical competence. Canine castration is a commonly performed procedure that is considered a Day One competency for a veterinarian. In this study, we sought to create and evaluate a canine pre-scrotal closed castration model and grading rubric using a validation framework of content evidence, internal structure evidence, and relationship with other variables. Veterinarians ( n = 8) and students ( n = 32) were recorded while they performed a castration on the model and provided survey feedback. A subset of the students ( n = 7) then performed a live canine castration, and their scores were compared with their model scores. One hundred percent of the veterinarians and 91% of the students reported that the model was helpful in training for canine castration. They highlighted several areas for continued improvement. Veterinarians’ model performance scores were significantly higher than students’, indicating that the model had adequate features to differentiate expert from novice performance. Students’ performance on the model strongly correlated with their performance of live castration ( r = .82). Surgical time was also strongly correlated ( r = .70). The internal consistency of model and live rubric scores were good at .85 and .94, respectively. The framework supported validation of the model and rubric. The canine castration model facilitated cost-efficient practice in a safe environment in which students received instructor feedback and learned through experience without the risk of negatively affecting a patient’s well-being. The strong correlation between model and live animal performance scores suggests that the model could be useful for mastery learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".