Evaluation of a New Technique for Preparation of Endothelial Grafts for Descemet Membrane Endothelial Keratoplasty
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare the Muraine technique, a relatively new method for preparing endothelial grafts for Descemet membrane endothelial keratoplasty (DMEK), with the current standard submerged cornea using backgrounds away (SCUBA) peeling technique. METHODS: This study was a prospective ex vivo investigation. In a wet-lab setting, 20 donor corneas were prepared for DMEK using The Muraine technique and 20 donor corneas using the SCUBA technique. In each of the technique groups, 10 corneas were prepared by a corneal surgeon and 10 were prepared by a corneal fellow. Primary outcome measures were the time needed to prepare endothelial grafts and the number of graft tears. RESULTS: In the SCUBA technique, median time to prepare grafts was shorter for both the surgeon (301 ± 85 seconds) and fellow (523 ± 58 seconds) compared with the Muraine technique (surgeon, 359 ± 83 seconds; fellow, 543 ± 44 seconds). However, these findings were not statistically significant (surgeon, P = 0.33; fellow, P = 0.24; pooled, P = 0.46). There was a statistically significant difference between surgeon time and fellow time for each technique (SCUBA technique, P = 0.0005; Muraine technique, P = 0.002). In the Muraine technique, there were 5 graft tears (surgeon = 2, fellow = 3), and no graft tears in the SCUBA technique, which was statistically significant (P = 0.047). CONCLUSIONS: The present study demonstrates that the SCUBA technique may be a more effective technique to prepare endothelial donor grafts for DMEK.
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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 | 0.001 |
| 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".