Artificial eye models: An opportunity to increase surgical training exposure in ophthalmology during and beyond the COVID-19 pandemic
Bibliographic record
Abstract
Purpose: To review 3 recently published articles regarding the face and content validity of 3 artificial eye models used for practicing ab-interno goniotomy, ab-interno canaloplasty, and anterior vitrectomy (SimulEYE® KDB/TrabEx, ABiC iTrack and A-Vit, InsEYEt, Westlake Village, CA) Participants: A total of 71 surveys were completed by ophthalmologists following a surgical simulation session at the 2019 Canadian Ophthalmological Society annual meeting. Methods: A 15-question survey to assess the face and content validity of the model was given immediately following the surgical simulation session. Responses to each survey question were recorded on a 5-point Likert scale ranging from (5) strongly agree to (1) strongly disagree. Results: Respondents rated statements regarding the models with a median response ranging from 5 (Strongly agree) to 3 (Neither agree nor disagree). Mann-Whitney U nonparametric analysis revealed no significant difference in responses between instructor vs. non-instructor or between prior experience vs. no prior experience in each study. The models received the highest combined ratings for their usefulness in training residents, utility in novice skill acquisition prior to in vivo procedures and higher likelihood of success with the procedure than theory and observation alone. The lowest aggregated score for the models was for realism of the models compared to a human cadaveric eye. Conclusion: Results from these studies suggest the SimulEYE KDB/TrabEx, AbiC iTrack and A-Vit models are a reasonably cost-effective solution for surgical simulation of ab-interno goniotomy, ab-interno canaloplasty, and anterior vitrectomy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".