Five-Year Safety and Efficacy of Femtosecond Laser–Assisted Descemet Membrane Endothelial Keratoplasty
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate 5-year safety and efficacy outcomes of femtosecond-enabled Descemet membrane endothelial keratoplasty (F-DMEK) in patients with Fuchs' endothelial dystrophy (FED). METHODS: This was a retrospective study, including patients with FED and cataract who underwent either F-DMEK (16 eyes of 15 patients) or manual DMEK (M-DMEK) (42 eyes of 37 patients) combined with cataract extraction. Outcome measures included visual acuity, graft detachment, graft survival, and endothelial cell loss. RESULTS: The average follow-up in F-DMEK and M-DMEK was 57.1 ± 12.4 months and 58.5 ± 17.3 months, respectively ( P = 0.757). The rates of primary failure (0% vs. 9.5%, P = 0.567), secondary failure (0% for both), and graft rejection (0% vs. 7.1%, P = 0.533) did not differ significantly between the groups. Improvement in best spectacle-corrected visual acuity was similar in F-DMEK and M-DMEK (0.32 ± 0.27 logarithm of the minimum angle of resolution and 0.35 ± 0.44 logarithm of the minimum angle of resolution, respectively, P = 0.165) and persisted at 2, 3, 4, and 5 years and at the last follow-up. The rates of graft detachment and rebubbling were significantly lower with 6.25% in F-DMEK and 33.3% in M-DMEK ( P = 0.035). Cell-loss rates were lower in F-DMEK compared with M-DMEK throughout the follow-up, significantly so up to 2 years with a difference of 8.6% at 1 year ( P = 0.023), 11.8% at 2 years ( P = 0.021), 7.6% at 3 years ( P = 0.088), 5.8% at 4 years ( P = 0.256), 13.6% at 5 years ( P = 0.169), and 7.1% at the final follow-up ( P = 0.341). CONCLUSIONS: F-DMEK had an excellent safety and efficacy profile which was maintained over 5 years of follow-up. Lower endothelial cell-loss rates in F-DMEK compared with M-DMEK may help extend the duration of graft survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".