Peripheral Blunt Dissection: Using a Microhoe-Facilitated Method for Descemet Membrane Endothelial Keratoplasty Donor Tissue Preparation
Bibliographic record
Abstract
PURPOSE: To describe a modified technique for Descemet membrane donor tissue preparation that facilitates the original Melles stripping technique. METHODS: Descemet membrane is prepared using a Rootman/Goldich modified Sloane microhoe, using a blunt instrument as opposed to a sharp blade or needle and begins dissection within the trabecular meshwork. The trabecular tissue is dissected for 360 degrees, and then Descemet membrane is stripped to approximately 50%. A skin biopsy punch is then used to create fenestration in the cornea, which is used to mark an "F." on the stromal side of Descemet membrane to aid in orientation of the graft. Trephination of the membrane is then performed and stripping is completed. The tissue is stained with 0.06% trypan blue and aspirated into an injector for insertion into the anterior chamber. RESULTS: Before converting to the technique described, 5 of 75 (6.7%) tissues were wasted and 7 of 75 (9.3%) tissues with radial tears were salvaged for use. Since converting to the new technique, only 1 of 171 (0.6%) (P = 0.01) tissues was wasted and 7 of 171 (4.1%) (P = 0.2) tissues with radial tears were salvaged. CONCLUSIONS: The peripheral blunt dissection technique offers an improvement over the technique originally described by Melles et al, as the incidence of tissue wastage and tears is lower, it is easy to learn, has low stress, and is reproducible. Combining this with a stromal surface letter mark ensures correct orientation of the tissue against the corneal stroma of the recipient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".