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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".