Endoscopy Training in Small Animal Internal Medicine: A Survey of Residency Training Programs in North America
Bibliographic record
Abstract
Competency in multiple endoscopic techniques is a major goal of small animal internal medicine (SAIM) residency programs. Training relies predominantly on mentored supervision of procedures performed on patients. Supplementation of this apprenticeship model with classroom sessions and hands-on laboratories can be advantageous to trainees and patients. Few veterinary resources describe supplemental training options, and no single source exists for mentors to consult for program development. The purpose of this study was to describe the supplemental training opportunities currently available to SAIM residents at academic hospitals in the US and Canada and to compare their timing during the residency, resident and faculty time commitment, and perceived helpfulness. Data were collected by an electronic survey distributed to one faculty member per institution. The response rate was 80% (24/30). Most programs (22/24; 92%) offered some form of supplemental training, including classroom sessions (9/24) and hands-on laboratories using physical models (7/24), virtual reality simulators (2/24), and cadaver (2/24) and anesthetized (2/24) dogs. Fifteen programs provided residents with the opportunity to attend external endoscopy workshops. Only three programs required any training prior to residents performing procedures on patients. There was considerable variability in training between programs, precluding statistical comparisons. The survey identified topics for classroom sessions and several inexpensive physical models, rated very or extremely helpful, that would be suitable for programs with limited budgets. A human-based virtual reality simulator was also rated highly by two programs. Comprehensive, external workshops evoked numerous positive comments with perceived value ranging from somewhat to extremely helpful.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".