Outcomes and Early Complications Using an Endothelium-in Pull-Through Descemet Membrane Endothelial Keratoplasty Technique With Preloaded Versus Surgeon-Loaded Donor Tissue in Fuchs Patients
Bibliographic record
Abstract
PURPOSE: This study aimed to compare outcomes and early complications using an endothelium-in pull-through Descemet membrane endothelial keratoplasty (DMEK) technique with preloaded versus surgeon-loaded donor tissue. METHODS: Data from 163 eyes of 125 patients at the Wilmer Eye Institute diagnosed with Fuchs endothelial corneal dystrophy who underwent DMEK with or without cataract extraction using surgeon-loaded tissue (n = 83) or preloaded tissue (n = 80) were reviewed. Best-corrected visual acuity and early postoperative complications including small graft detachment (less than one third of the graft area), large graft detachment (more than one third), graft failure, and rebubbling were compared. RESULTS: Baseline characteristics including age, sex, and visual acuity were not statistically different between the groups. Small graft detachment was observed in 18.1% of the surgeon-loaded and 22.5% of the preloaded group ( P = 0.48), whereas large detachment occurred in 12.0% and 5.0%, respectively ( P = 0.11). Among these, rebubbling was performed in 18 (21.7%) in the surgeon-loaded compared with 12 (15.0%) in the preloaded group ( P = 0.27). The rebubbling rate of the combined procedure (cataract surgery and DMEK) was 21.8% and of DMEK alone was 7.7% ( P = 0.048). Primary graft failure occurred in 2 surgeon-loaded cases (2.4%) and 1 preloaded case (1.3%) ( P = 0.58). There was no difference in postoperative best-corrected visual acuity at 1 year (logarithm of the minimum angle of resolution 0.21 ± 0.25 for the surgeon-loaded vs. 0.16 ± 0.16 for the preloaded group, P = 0.23). CONCLUSIONS: DMEK surgery using preloaded endothelium-in tissue has comparable outcomes with surgeon-loaded endothelium-in tissue. However, there was a trend toward the lower rebubbling rate in DMEK alone compared with combined procedures.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".