Differences in Surgical Performance of Internal Limiting Membrane Peeling for Macular Hole Repair Between Supervised Vitreoretinal Fellows and Vitreoretinal Faculty at a Single Institution
Bibliographic record
Abstract
Purpose: To investigate the differences in surgical maneuvers between vitreoretinal fellows and experienced vitreoretinal surgeons (attendings) when performing internal limiting membrane (ILM) peel during macular hole (MH) surgery and repair. Methods: Prospective case series. Macular hole surgeries performed by fellows and attendings at St Michael’s Hospital (Toronto, Canada) were recorded during a 12-month period. Evaluation of recordings was masked. Total peel time (TPL) in seconds, total movement attempts initiating and extending ILM flaps, intrasurgical complications, and surgical efficiency (ratio of approaches leading to case progression to total approaches) were quantified. Results: A total of 145 surgeries were evaluated; 44 met inclusion and exclusion criteria. Of the 44 cases, 25 were performed by fellows and 19 by attendings. Mean TPL was shorter for attendings (336 vs 506 seconds, P = .0032). Attendings had a lower average total movement attempts (32.2 vs 43.2, P = .045) and average flap initiation attempts (16.1 vs 23.3, P = .042). Surgical efficiency was better for attendings (45% vs 37% of approaches led to case progression, P = .038). There was no significant difference between groups in total flap extension attempts or intrasurgical complications. Conclusions: Compared to fellows, attendings peel ILM in MH surgery faster, more efficiently with a lower number of flap initiation attempts and total movements.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".