Evaluation of a method to assess digitally recorded surgical skills of novice veterinary students
Bibliographic record
Abstract
OBJECTIVE: To evaluate a method to assess surgical skills of veterinary students that is based on digital recording of their performance during closure of a celiotomy in canine cadavers. SAMPLE POPULATION: Second year veterinary students without prior experience with live animal or simulated surgical procedure (n = 19) METHODS: Each student completed a 3-layer closure of a celiotomy on a canine cadaver. Each procedure was digitally recorded with a single small wide-angle camera mounted to the overhead surgical light. The performance was scored by 2 of 5 trained raters who were unaware of the identity of the students. Scores were based on an 8-item rubric that was created to evaluate surgical skills that are required to close a celiotomy. The reliability of scores was tested with Cronbach's α, intraclass correlation, and a generalizability study. RESULTS: The internal consistency of the grading rubric, as measured by α, was .76. Interrater reliability, as measured by intraclass correlation, was 0.64. The generalizability coefficient was 0.56. CONCLUSION: Reliability measures of 0.60 and above have been suggested as adequate to assess low-stakes skills. The task-specific grading rubric used in this study to evaluate veterinary surgical skills captured by a single wide-angle camera mounted to an overhead surgical light produced scores with acceptable internal consistency, substantial interrater reliability, and marginal generalizability. IMPACT: Evaluation of veterinary students' surgical skills by using digital recordings with a validated rubric improves flexibility when designing accurate assessments.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".