Comparison of Femtosecond Laser-Enabled Descemetorhexis and Manual Descemetorhexis in Descemet Membrane Endothelial Keratoplasty
Bibliographic record
Abstract
PURPOSE: To introduce a novel method to perform descemetorhexis in Descemet membrane endothelial keratoplasty (DMEK) using the femtosecond laser and to compare it with Descemet membrane endothelial keratoplasty performed with manual descemetorhexis (M-DMEK). METHODS: A retrospective medical chart review of 2 groups of patients who underwent DMEK surgery combined with cataract surgery secondary to Fuchs corneal endothelial dystrophy and cataract: 17 patients underwent femtosecond laser-enabled descemetorhexis Descemet membrane endothelial keratoplasty (FE-DMEK) and 89 patients underwent DMEK surgery with M-DMEK. Best spectacle-corrected visual acuity, endothelial cell density (ECD), graft detachment rate, and complications were compared. RESULTS: Average age of the 106 patients (64 women and 42 men) was 68 ± 11 years. Postoperative best spectacle-corrected visual acuity was 0.19 ± 0.13 logarithm of the minimum angle of resolution in the FE-DMEK group and 0.35 ± 0.48 logarithm of the minimum angle of resolution in the M-DMEK group (P = 0.218). One day after surgery, there were no significant graft detachments in the FE-DMEK group, compared with 20% graft detachment rate in the M-DMEK group (P = 0.041). Rebubbling was performed in 17% of eyes in the M-DMEK group compared with none in the FE-DMEK group (P = 0.066). The mean endothelial cell count in the FE-DMEK and M-DMEK groups at 6 months after surgery were 2105 ± 285 cells per square millimeter (24% cells loss) and 1990 ± 600 cells per square millimeter (29% cells loss), respectively (P = 0.579). CONCLUSIONS: FE-DMEK shows efficacy similar to that of M-DMEK with apparently less graft detachment and reduced need for rebubbling.
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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 | 0.000 |
| 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 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".