Peripheral Blunt Dissection: Using a Microhoe-Facilitated Method for Descemet Membrane Endothelial Keratoplasty Donor Tissue Preparation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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