Evaluation of a New Technique for Preparation of Endothelial Grafts for Descemet Membrane Endothelial Keratoplasty
Why this work is in the frame
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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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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 | 0.002 | 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) | 0.000 | 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 it