Three-Year Outcome Comparison Between Femtosecond Laser-Assisted and Manual Descemet Membrane Endothelial Keratoplasty
Bibliographic record
Abstract
PURPOSE: To evaluate 3-year outcomes of femtosecond laser-assisted Descemet membrane endothelial keratoplasty (F-DMEK) compared with manual Descemet membrane endothelial keratoplasty (M-DMEK) in patients with Fuchs endothelial corneal dystrophy (FECD). METHODS: A retrospective, interventional study, including eyes with FECD and cataract that underwent either F-DMEK or M-DMEK combined with cataract extraction at either the Toronto Western Hospital or Kensington Eye Institute, and that had at least 18 months' follow-up was conducted. EXCLUSION CRITERIA: complicated anterior segments, previous vitrectomy, previous keratoplasty, corneal opacity, or any other visually significant ocular comorbidity. RESULTS: Included were 16 eyes of 15 patients in the F-DMEK group (average follow-up 33.0 ± 9.0 months) and 45 eyes of 40 patients in the M-DMEK group (average follow-up 32.0 ± 7.0 months). There were no issues with the creation of femtosecond descemetorhexis (in the F-DMEK group)-all descemetorhexis cuts were complete. Best spectacle-corrected visual acuity improvement did not differ significantly between the groups at 1, 2, and 3 years (P = 0.849, P = 0.465 and P = 0.936, respectively). Rates of significant detachment in F-DMEK and M-DMEK were 1 of 16 eyes (6.25%) and 16 of 45 eyes (35.6%) (P = 0.027). Rebubbling rates were 1 of 16 eyes (6.25%) and 15 of 45 eyes (33.3%) (P = 0.047). Cell-loss rates following F-DMEK and M-DMEK were 26.8% and 36.5% at 1 year (P = 0.042), 30.5% and 42.3% at 2 years (P = 0.008), 37% and 47.5% at 3 years (P = 0.057), respectively. Graft failure rate was 0% in F-DMEK and 8.9% in M-DMEK (all were primary failures; P = 0.565). CONCLUSIONS: F-DMEK showed good efficacy with reduced detachment, rebubble, and cell-loss rates, compared with M-DMEK.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".