Validation of a Realistic Simulator for Veterinary Gastrointestinal Endoscopy Training
Bibliographic record
Abstract
This article reports on the face, content, and construct validity of a new realistic composite simulator (Simuldog) used to provide training in canine gastrointestinal flexible endoscopy. The basic endoscopic procedures performed on the simulator were esophagogastroduodenoscopy (EGD), gastric biopsy (GB), and gastric foreign body removal (FBR). Construct validity was assessed by comparing the performance of novices (final-year veterinary students and recent graduates without endoscopic experience, n=30) versus experienced subjects (doctors in veterinary medicine who had performed more than 50 clinical upper gastrointestinal endoscopic procedures as a surgeon, n=15). Tasks were scored based on completion time, and specific rating scales were developed to assess performance. Internal consistency and inter-rater agreement were assessed. Face and content validity were determined using a 5-point Likert-type scale questionnaire. The novices needed considerably more time than the experts to perform EGD, GB, and FBR, and their performance scores were significantly lower (p<.010). Inter-rater agreement and the internal validity of the rating scales were good. Face validity was excellent, and both groups agreed that the endoscopy scenarios were very realistic. The experts highly valued the usefulness of Simuldog for veterinary training and as a tool for assessing endoscopic skills. Simuldog is the first validated model specifically developed to be used as a training tool for endoscopy techniques in small animals.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".