Evaluation of a Human Virtual-Reality Endoscopy Trainer for Teaching Early Endoscopy Skills to Veterinarians
Bibliographic record
Abstract
Competency in flexible endoscopy is a major goal of small animal internal medicine residency training programs. Hands-on laboratories to teach entry-level skills have traditionally used anesthetized laboratory dogs (live dog laboratory [LDL]). Virtual-reality endoscopy trainers (VRET) are used for this purpose in human medicine with the clear benefits of avoiding live animal use, decreasing trainee stress, and allowing repeated, independent training sessions. However, there are currently no commercially available veterinary endoscopy simulators. The purpose of the study was to determine whether a human VRET can be a reasonable alternative to a LDL for teaching early veterinary endoscopy skills. Twelve veterinarians with limited or no endoscopy experience underwent training with a VRET ( n = 6) or a LDL ( n = 6), performed two recorded esophagogastroduodenoscopies (EGD) on anesthetized dogs for evaluation purposes (outcomes laboratory), and then underwent training with the alternative method. Participants completed questionnaires before any training and following each training session. No significant differences were found between training methods based on: measured parameters from the outcomes laboratory, including duration of time to perform EGD; evaluators’ assessment of skills; and, assessment of skills through blinded review of the esophageal portion of EGD recordings. The VRET was less stressful for participants than the LDL ( p = .02). All participants found that the VRET was a useful and acceptable alternative to the LDL for training of early endoscopy skills. Based on this limited study, VRET can serve as a reasonable alternative to LDL for teaching endoscopy skills to veterinarians.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".