Face and content validity of an artificial eye model for Ab-Interno Goniotomy
Bibliographic record
Abstract
PURPOSE: To determine the face and content validity of an artificial eye model for ab-interno goniotomy (SimulEYE KDB model, InsEYEt, Westlake Village, CA) by surveying ophthalmologists with varying experience using a Kahook Dual Blade (KDB; New World Medical, Rancho Cucamonga, CA, USA) following a 90-min wet-lab course using the model. PARTICIPANTS: Overall 13 ophthalmologists participated following a surgical simulation session on goniotomy using the goniotomy blade at the 2019 Canadian Ophthalmological Society annual meeting. METHODS: A 17-question survey to assess the face and content validity of the model was given immediately following the surgical simulation session on goniotomy using the goniotomy blade. Responses to each survey question were recorded on a 5-point Likert scale ranging from (1) strongly agree to (5) strongly disagree. RESULTS: nonparametric analysis revealed no significant difference in responses between instructor vs. non-instructor or between prior experience vs. no prior experience for any of the survey statements. The model received highest survey ratings for utility in training residents, acquisition of surgical skills, accessibility, and higher likelihood of success with the procedure than theory and observation alone. Lowest ratings were for realism of the model compared to a human cadaveric eye. CONCLUSION: Our results suggest the SimulEYE KDB model is a reasonably cost-effective solution for simulating angle-based surgeries. Additionally, our project shows that experienced ophthalmologists found the artificial eye models useful and helpful for angle-based surgery training.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".