Comparison of 2 canine celiotomy closure models for training novice veterinary students
Bibliographic record
Abstract
OBJECTIVE: To compare a low-fidelity foam and fabric (FF) model to a high fidelity silicone (SI) model for teaching canine celiotomy closure. STUDY DESIGN: Prospective blinded comparison of learning outcomes. SAMPLE POPULATION: Second-year veterinary students who had never performed surgery as a primary surgeon (n = 46) and veterinarians experienced in performing canine celiotomy (n = 10). METHODS: Veterinary students performed a digitally recorded celiotomy closure on a canine cadaver before and after participation in 4 facilitated laboratory training sessions on their randomly assigned model. Recordings were scored by masked, trained educators with an 8-item task-specific rubric. Students completed surveys evaluating the models. Experienced veterinarians tested the models and provided feedback on their features. RESULTS: Completed pretest and posttest recordings were available for 38 of 46 students. Students' performance improved regardless of the model used to practice (P = .04). The magnitude of improvement did not differ between the 2 groups (P = .10). All students (n = 46) described their models favorably. Ninety percent of veterinarians thought both models were helpful for training students and gave similar ratings on all measures except for realism, which was rated higher for the SI model's skin (median, agree) compared with the FF model (median, neutral, P = .02). CONCLUSION: Model-based training was effective at improving students' surgical skills. Less experienced learners achieved similar skill gains after practicing with FF or SI models. CLINICAL SIGNIFICANCE: The acquisition of surgical skills required to perform celiotomy closure in companion animals occurs similarly well on models made of foam and fabric or of silicone, providing flexibility in model selection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".