Global trends and practice patterns in virtual reality simulation training for ophthalmic surgery: an international survey use of virtual reality simulation training around the world
Bibliographic record
Abstract
This global survey investigated the use of virtual reality simulation (VRS) in ophthalmological surgery education. Questionnaires were distributed to authors of publications and directors of centers using VRS for surgical education in ophthalmology, then forwarded to residents and fellows of their team for completion. Out of 1845 questionnaires sent across 36 countries, 170 responses from 26 countries were analyzed, primarily from residents and fellows (75%). Mean access duration to VRS was 3.6 years, often at University Hospitals (43.5%). Notably, 12% of respondents traveled an average of 550 km to access VRS. The EyeSi VR Magic was the most frequently reported simulator (80%, mainly in Europe/North America), followed by HelpMeSee (48%, primarily in Europe/India/Madagascar). In 25 training centers across 12 countries, VRS was a mandatory prerequisite for patient access, functioning as a "surgical license". A larger number of training centers (49 from 19 countries) favored such mandatory training. Junior surgeons perceived a greater impact of VRS on their surgical practice compared to senior surgeons (p = 0.032). The study concludes that while VRS holds a significant role in postgraduate ophthalmic surgical training, its access is unequal worldwide. Broader implementation and standardized practices could improve and maintain high educational standards in this field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".