Comparison of Femtosecond Laser-Enabled Descemetorhexis and Manual Descemetorhexis in Descemet Membrane Endothelial Keratoplasty
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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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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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