Hurdles of Cataract Surgery: Veterinary Ophthalmology Residents’ Perspective (Part B)
Bibliographic record
Abstract
Abstract The purpose of this study was to describe the perceived preparedness for performing cataract surgery of veterinary ophthalmology residents’ who are currently enrolled in, or recently graduated from, veterinary academic or private practice institutions. A descriptive survey was distributed online to 127 residents at academic and private practice training programs in the United States. The survey included items about educational resources available for residents and techniques commonly taught during cataract surgery. Residents were asked to describe their perceived preparedness in performing various surgical steps or techniques, difficulty of each surgical step, and the available educational resources. Thirty-five (27.5%) residents completed the survey and were included in this study. Residents who had access to wet labs gained surgical competency in creating a clear corneal incision, capsulorhexis, and wound closure. They reported sculpting with the phacoemulsification handpiece, quadrant or cortical removal, and capsulorhexis as most difficult and were not as prepared or a little prepared in performing capsulorhexis and sculpting during active phacoemulsification. When comparing residents’ perceived competency before and after their first surgical experience, there was a significant change in their ability to perform all surgical steps except hydrodissection ( p < .05). Cataract surgery is one of the more advanced surgical skills obtained during residency training. Supervised wet lab time improves a resident’s preparedness for executing certain surgical steps. However, further research is needed to determine whether educational resources such as structured curriculum or virtual simulation may improve residents’ preparedness for executing surgical steps not easily replicated in a wet lab.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".