Three-Year Outcome Comparison Between Femtosecond Laser-Assisted and Manual Descemet Membrane Endothelial Keratoplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 it