Outcomes of Descemet Membrane Endothelial Keratoplasty in Aphakic and Aniridic Patients
Bibliographic record
Abstract
PURPOSE: To evaluate the outcomes of Descemet membrane endothelial keratoplasty (DMEK) in aphakic and aniridic eyes. METHODS: A retrospective chart review of either aphakic or aniridic patients who underwent DMEK at Toronto Western Hospital, Canada, between 2015 and 2019 was performed. Demographic characteristics, intraoperative and postoperative complications, and best corrected visual acuity (BCVA) were analyzed. RESULTS: Nine eyes of 9 patients, aged 51.0 ± 8.6 years, were included (3 aniridic, 5 aphakic, and 1 combined). The average follow-up was 15.7 ± 12.7 months. The best corrected visual acuities before surgery and 3 and 6 months after surgery were 1.28 ± 0.47, 1.33 ± 0.98, and 1.03 ± 0.56 LogMAR, respectively. Six eyes (67%) had graft detachment, with 3 of them larger than 30% of the graft area. One eye (11%) developed hyphema. The overall failure rate was 88% (8 of 9 eyes), meaning only one was viable at the last follow-up. Primary graft failure was seen in 4 eyes (44%) after detachment (n = 3) and intraoperative hyphema (n = 1). Secondary failure occurred in 4 eyes (44%) at 7, 12, 15, and 36 months. The secondary failure at 36 months was after rejection. Failures were managed with penetrating keratoplasty (n = 2), repeat DMEK (n = 3), Descemet stripping automated endothelial keratoplasty (n = 1), and observation because of poor vision potential (n = 2). Cumulative graft survival probabilities at 12 and 24 months were 44% and 17%, respectively. CONCLUSIONS: Aniridic and aphakic patients experienced unacceptably high detachment and failure rates after DMEK. Before performing DMEK, the risks and benefits should be carefully weighed and perhaps other keratoplasty techniques should be used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".