Comparison of Simulated Surgical Skills Using Different Camera Aperture Settings for Digitally Assisted Vitreoretinal Surgery
Bibliographic record
Abstract
Purpose: This cross-sectional, randomized, single-blinded study evaluates simulated surgical skills during digitally assisted vitreoretinal surgery (DAVS) with different camera aperture settings to determine maximal surgical performance and preference. Methods: Fifteen retina fellows performed 3 different surgical simulator tests with a DAVS visualization system. To evaluate maximal surgical performance and preference, DAVS was evaluated at different camera apertures (30% and 75%) with a fixed magnification (12×) and television (TV) viewing distance (1.2 m [4 ft]). Comparisons were made of the time needed to complete each test as well as the number of errors. At the end of the 3 tests, participants were asked for their camera aperture preference. Results: The time it took to perform each simulated surgical test was significantly less with the 30% aperture than with the 75% aperture ( P = .036). Furthermore, the number of errors made with the 30% aperture was always less than with the 75% aperture ( P = .009). After completing all tests, evaluation of surgeon preference for camera aperture settings showed that 83% of the participants preferred the 30% aperture over the 75% aperture. Conclusions: Optimization of DAVS parameters directly influences surgical performance. A 30% camera aperture in DAVS outperforms larger camera apertures, not only in depth of field and lateral resolution values, but also in simulated surgical skills. A smaller number of errors, as well as less time needed to complete tests, was achieved with these settings. We suggest that the optimal settings for DAVS enhancement are a 30% camera aperture and a TV viewing distance of 1.2 m.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".