Comparison of 2 training programs for basic laparoscopic skills and simulated surgery performance in veterinary students
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of 2 training curricula on laparoscopic skills and performance of simulated surgery in veterinary students. STUDY DESIGN: Prospective study. SAMPLE POPULATION: Veterinary students (n = 33) with no prior hands-on experience in minimally invasive surgery. METHODS: Basic laparoscopic skills (BLS) were assessed based on 5 modified McGill inanimate system for training and evaluation of laparoscopic skills. Motion metrics and an objective structured assessment of technical skills (OSATS) were used to evaluate surgical skills during a simulated laparoscopic cholecystectomy performed in an augmented reality simulator. Students were randomly assigned to either skill-based (group A) or procedural-based (group B) training curriculum. Both tests were performed prior to and after a 10-session training curriculum. RESULTS: Post-training BLS results were improved in both training groups (P < .001). Seven participants completed both presimulated and postsimulated laparoscopic cholecystectomy, preventing paired analysis. Based on motion metrics analysis, participants completed tasks in a shorter time (P = .0187), and with better economy of movement (P = .0457) after training. No difference was detected in OSATS before and after training. CONCLUSION: Both training curricula improved BLS, but significant differences were not detected between the procedural-based training program and basic skills training alone in veterinary students. Motion metrics such as time, economy of movement, and instrument path were superior to an OSATS, when assessing surgical performance. Further studies are needed to compare the effects of different simulators on the training of veterinarians with diverse laparoscopic surgical experience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".