Evaluating veterinary student skill acquisition on a laparoscopic suturing exercise after simulation training
Bibliographic record
Abstract
OBJECTIVE: To determine whether veterinary students could train to a predefined proficiency level on a simulated intracorporeal laparoscopic suturing task. STUDY DESIGN: Single group preinterventional and postinterventional study. SAMPLE POPULATION: Ten veterinary students. METHODS: Ten veterinary students completed a questionnaire about prior experiences and watched a 7-minute demonstration video prior to performing a laparoscopic intracorporeal suture task on a simulator. Participants were tested at pretraining and for a period of 8 weeks. Overall group improvement in scores and time to completion (seconds) from pretraining to final testing was analyzed by using a Wilcoxon matched-pairs signed-rank test. The same variables were compared among individuals with different background experiences (eg, video game experience) by using a Mann-Whitney U test. The average number of repetitions to reach proficiency was recorded. RESULTS: All participants reached the predefined proficiency level on the Fundamentals of Laparoscopic Surgery intracorporeal suture task. The average number of repetitions required to reach proficiency was 18 ± 7, and there was significant improvement in both time to completion (seconds) and scores from pretraining to final testing (P = .005). The number of repetitions required to reach proficiency, pretraining times, final times, pretraining scores, and final scores did not differ among veterinary students with different background experiences. CONCLUSION: Veterinary students naïve to laparoscopic surgery can learn the technical skills required to perform a simulated intracorporeal suture through repetitive, self-directed practice on a laparoscopic box trainer regardless of prior experiences (eg, videogame experience, craft experience, chopstick use, etc). CLINICAL SIGNIFICANCE: Simulation offers an adequate platform for the standardized training of laparoscopic skills in veterinary students and likely novice laparoscopic surgeons.
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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) | 0.001 | 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".