Training Veterinary Ophthalmology Residents on Cataract Surgery (Part A: Diplomate’s Survey)
Bibliographic record
Abstract
The purpose of this study was to explore the teaching methods used to train residents in cataract surgery at academic and private practice institutions. A descriptive survey was distributed online to 186 active supervising diplomates of the American Board of Veterinary Ophthalmologists (ABVO) listserv. The survey included items about various educational resources and techniques available to ophthalmology residents when performing cataract surgery. Thirty-seven (19.9%) supervising diplomates completed the survey. Most supervising diplomates (29, 78.4%) required supervised practice in the wet lab. Fourteen supervising diplomates (37.8%) provided mandatory cataract surgery lectures. Nine diplomates (24.3%) required their residents to attend a formal phacoemulsification wet lab course. There was no difference in the number of diplomates who allowed their residents to perform cataract surgery as the primary surgeon during their second year compared to any other year. Thirty-three surveyed diplomates supported the idea of creating an assessment tool to improve surgical competency. This article describes the current trends in veterinary ophthalmology residency education and provides support for the creation of an assessment tool to improve surgical competency. The goal is to stimulate future research on how educators can effectively train veterinary ophthalmology residents to improve surgical proficiency.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".