Management of Descemet Membrane's Folds After Deep Anterior Lamellar Keratoplasty: Descemet Membrane—Tucking Technique
Bibliographic record
Abstract
PURPOSE: To describe a surgical maneuver that allows for correction of central Descemet membrane (DM) folds at the end of a deep anterior lamellar keratoplasty (DALK) procedure. We term the present technique "DM tucking." METHODS: A blunt tip spatula is introduced vertically into the trephination cut, 90 degrees away from the main direction of the DM folds, and advanced until it touches the host layer. Gentle pressure is applied, resulting in tucking of the redundant host layer toward the periphery. The tucking maneuver is repeated at different clock hours until a regular graft-host interface is obtained. RESULTS: We applied the present technique to several DALK procedures performed for keratoconus, and found it to be safe and effective. CONCLUSIONS: DALK is the procedure of choice for the surgical treatment of corneal stromal diseases with a healthy endothelium, such as keratoconus. DM folds are a possible complication after DALK in patients with advanced corneal ectasia, arising from the compression of the redundant host DM by the donor graft, once it is sutured to the recipient. DM folds after DALK, when they involve the visual axis, cause permanent visual disturbances DM tucking allows the displacement of the central DM folds toward the graft margin, where they do not affect the patient's quality of vision.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".