Descemet membrane endothelial keratoplasty in patients with prior glaucoma surgery
Why this work is in the frame
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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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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