Impact of Surgical Learning Curve in Descemet Membrane Endothelial Keratoplasty on Visual Acuity Gain
Why this work is in the frame
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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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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) | 0.000 | 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 it