Descemet membrane endothelial keratoplasty in patients with prior glaucoma surgery
Bibliographic record
Abstract
Objective: To present outcomes of Descemet membrane endothelial keratoplasty (DMEK) in eyes with prior trabeculectomy or a glaucoma drainage device (GDD). Methods: A retrospective case series, including patients that had previously undergone trabeculectomy and/or GDD implantation, who later underwent DMEK between 2013 and 2016 at Toronto Western Hospital and the Kensington Eye Institute. Outcome measures: best spectacle-corrected visual acuity (BSCVA), endothelial cell (EC) density, intraoperative and postoperative complications. Results: Twenty-seven eyes of 27 patients were included. All DMEK procedures were uneventful. Mean follow-up time was 14.6 ± 6.1 months. In eyes with no visually limiting comorbidities ( n = 16), BSCVA improved from 1.34 ± 0.65 logMAR (Snellen equivalent ~20/440) preoperatively to 0.51 ± 0.24 logMAR (Snellen equivalent ~20/65) and 0.50 ± 0.33 logMAR (Snellen equivalent ~20/65) at 6 and 12 months, respectively ( p < 0.001 for both). In eyes with visually limiting comorbidities ( n = 11), BSCVA improved from 1.92 ± 0.72 logMAR (Snellen equivalent ~20/1665) preoperatively to 1.43 ± 0.83 logMAR (Snellen equivalent ~20/540) and 1.37 ± 0.99 logMAR (Snellen equivalent ~20/470) at 6 and 12 months, respectively ( p = 0.008 and p = 0.037). Graft detachment rate was 24.1% and rebubble rate was 17.2%. Primary and secondary graft failure rates were 3.7% and 10.3%, respectively. Rejection rate was 17.2%. EC-loss rate at 6 months and 12 months was 36.7% and 50.5%, respectively. Conclusions: DMEK performed in eyes with previous trabeculectomy or a GDD is more challenging than conventional DMEK, but has good outcomes. Higher rates of graft rejection and secondary graft failure in this setting should be further evaluated in long-term studies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".