Impact of Surgical Learning Curve in Descemet Membrane Endothelial Keratoplasty on Visual Acuity Gain
Bibliographic record
Abstract
PURPOSE: To evaluate the learning curve for graft preparation and graft unrolling during Descemet membrane endothelial keratoplasty (DMEK) and to assess the evolution of visual acuity gain and percentage cell loss with experience. METHODS: The first 109 DMEK procedures performed by a single surgeon (A.S.) at the Rothschild Foundation Ophthalmology Hospital in Paris, France, between March 2012 and November 2014 were included. Best-corrected visual acuity and endothelial cell density were recorded preoperatively and again 1 week, 1 month, 3 months, and 6 months after DMEK. Donor age and ECC were registered. Graft preparation time and graft unrolling time were assessed using video recording. Incidence and types of complications were noted. RESULTS: The number of cases necessary to reach 90% of the plateau of the learning curve was 68 for preparation time and 46 for unrolling time in this model. There was no correlation between the best-corrected visual acuity gain at 6 months postsurgery and the learning curve. The percentage cell loss was found to be significantly lower with experience (R = 0.17, P = 0.0011). CONCLUSIONS: Surgical experience allowed faster graft preparation and faster unrolling time in DMEK. Neither experience nor percentage cell loss influenced postoperative visual acuity gain. The number of procedures needed to reach a good standard of care was estimated to be 50 in our patient database.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".