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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".