Descemet Membrane Endothelial Keratoplasty for Failed Deep Anterior Lamellar Keratoplasty: A Case Series
Bibliographic record
Abstract
PURPOSE: To suggest a new surgical approach for posterior opacities or persistent Descemet membrane (DM) detachment in failed deep anterior lamellar keratoplasty (DALK) and to report the outcome of Descemet membrane endothelial keratoplasty (DMEK) in a series of patients with DALK failure. METHODS: In this retrospective case series of patients who underwent DMEK for failed DALK at Toronto Western Hospital, 4 patients with failed DALK who underwent DMEK surgery were included. In all 4 cases, big bubble formation during initial DALK surgery had failed and the surgical technique was converted to manual dissection using the Melles technique. In 2 cases, the descemetorhexis in DMEK surgery was performed manually, and in 2 cases, the descemetorhexis was performed with the assistance of the femtosecond laser. RESULTS: Four eyes of 4 patients aged 70 ± 4 years were included. Average follow-up time was 9 ± 5 months. Indications for DALK surgery were corneal dystrophy in 2 patients and corneal scars in the other 2. DALK failure was due to persistent DM detachment that created a double chamber in 2 patients and due to posterior lamellar haze in the other 2. After DMEK surgery, 2 patients had graft detachment and required rebubbling. In 1 patient, the DMEK scroll was attached after 1 rebubble attempt, and in the other patient, a second rebubble attempt was needed. The final visual acuities were 20/40, 20/50, 20/70, and 20/200 (because of dense cataract). All procedures were uneventful. CONCLUSIONS: DMEK surgery may be effective in managing DALK failure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".