Comparison of Attempts Needed for Veterinary Students to Reach Proficiency in a Basic and Advanced Robotic Simulator Task
Bibliographic record
Abstract
Surgical training of students is one of the most difficult aspects of veterinary medical education. Competing interests of house officers, owners' wishes, and ethical concerns have led to increasing use of inanimate models for basic surgical skills training. Robotic simulators have benefits for psychomotor training but have not been previously investigated in veterinary medicine. Understanding the necessary practice time for new training devices is important when determining their potential value. The aim of this study was to compare the number of attempts needed for veterinary students to reach proficiency in both a basic and advanced robotic simulator task, and to assess the predictive nature of performance variables. Each student performed a basic and advanced tasks on the Mimic dV-Trainer™ until proficiency was reached. Students required a median of eight attempts (95% CI = 7-8, range: 6-11) to reach proficiency for the basic task versus 22 attempts (95% CI = 20-26, range: 11-62) for the advanced task. The median time required to complete training for the basic and advanced task was 13.5 minutes (range: 8-24 minutes) and 26.5 minutes (range: 11-82 minutes) respectively. The difference in task attempts supports the training protocol and confirms proficiency can be attained in a short period of time. The number of attempts to reach proficiency correlated with specific performance variables that can be used by educators to aid in training students on a robotic simulator. Continued investigations on robotic simulators should be performed to investigate their use in improving psychomotor skills in veterinary students.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".