Is there inter‐procedural transfer of skills in intraocular surgery? A randomized controlled trial
Bibliographic record
Abstract
PURPOSE: To investigate how experience in simulated cataract surgery impacts and transfers to the learning curves for novices in vitreoretinal surgery. METHODS: Twelve ophthalmology residents without previous experience in intraocular surgery were randomized to (1) intensive training in cataract surgery on a virtual-reality simulator until passing a test with predefined validity evidence (cataract trainees) or to (2) no cataract surgery training (novices). Possible skill transfer was assessed using a test consisting of all 11 vitreoretinal modules on the EyeSi virtual-reality simulator. All participants repeated the test of vitreoretinal surgical skills until their performance curve plateaued. Three experienced vitreoretinal surgeons also performed the test to establish validity evidence. Analysis with independent samples t-tests was performed. RESULTS: The vitreoretinal test on the EyeSi simulator demonstrated evidence of validity, given statistically significant differences in mean test scores for the first repetition; experienced surgeons scored higher than novices (p = 0.023) and cataract trainees (p = 0.003). Internal consistency for the 11 modules of the test was acceptable (Cronbach's α = 0.73). Our findings did not indicate a transfer effect with no significant differences found between cataract trainees and novices in their starting scores (mean ± SD 381 ± 129 points versus 455 ± 82 points, p = 0.262), time to reach maximum performance level (10.7 ± 3.0 hr versus 8.7 ± 2.8 hr, p = 0.265), or maximum scores (785 ± 162 points versus 805 ± 73 points, p = 0.791). CONCLUSION: Pretraining in cataract surgery did not demonstrate any measurable effect on vitreoretinal procedural performance. The results of this study indicate that we should not anticipate extensive transfer of surgical skills when planning training programmes in intraocular surgery.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".