Development and Evaluation of a Canine Laparoscopic Simulator for Veterinary Clinical Training
Bibliographic record
Abstract
Human laparoscopic simulators have been used in medical education for minimally invasive surgery (MIS) in the past years. Simulator-based laparoscopic training has attracted much interest because unique skills have to be learned not only by surgeons in training but also by surgeons in practice. MIS forces the surgeon to adapt to monocular vision and decreased tactile sensation and entails training and improving hand-eye and hand-hand coordination. Those skills require a learning curve that could be overcome gradually with use of simulators. The Canine Laparoscopic Simulator (CLS) for laparoscopic training was developed based on the working and optical space obtained from computed tomography (CT) scan images of three Beagle dogs. Thirty veterinarians (expert group, n=7; novice group, n=23) performed basic laparoscopic exercises in one training session on the CLS. During the performance of the exercises, an experienced laparoscopic veterinarian assessed all the tasks. Afterwards, participants were asked to complete an anonymous survey describing their experience. Most participants expressed positive opinions about the design and usability of the CLS. There were no significant differences between the two groups' opinions. The CLS showed good preliminary acceptance in the basic laparoscopy tasks by veterinarians. They perceived it to be a good training tool, and these results suggest that CLS is an engaging tool for education but still has some limitations inherent in training boxes. Further studies would be needed to establish the validity of training programs performed in the CLS.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".