Four-Year Survival Comparison of Endothelial Keratoplasty Techniques in Patients With Previous Glaucoma Surgery
Bibliographic record
Abstract
PURPOSE: To compare 4-year survival outcomes of Descemet membrane endothelial keratoplasty (DMEK) and Descemet-stripping automated endothelial keratoplasty (DSAEK) in eyes with previous glaucoma surgery. METHODS: This is a retrospective, comparative case series, including patients with previous trabeculectomy or glaucoma drainage device implantation, who later underwent either DMEK (n = 48) or DSAEK (n = 41). Follow-up was limited to 12 to 60 months to prevent bias. Primary outcomes were graft survival and rejection. Secondary outcomes were best spectacle-corrected visual acuity (BSCVA), detachment/rebubble, endothelial cell loss, and intraocular pressure elevations. RESULTS: Baseline characteristics, follow-up duration, and preexisting glaucoma parameters did not differ significantly between the groups. Graft survival probability after DMEK and DSAEK was 75% and 75% at 1 year, 63% and 50% at 2 years, 49% and 44% at 3 years, 28% and 33% at 4 years, and 28% and 29% at 5 years, respectively (P = 0.899 between the groups). Graft rejection rates were 20.8% and 19.5%, respectively (P = 1.000). Primary failure, rebubbling, endothelial cell loss, and intraocular pressure elevation did not differ significantly between the groups. Preoperative BSCVA did not differ between the groups (P = 0.821). Postoperative BSCVA was significantly better in the DMEK group at 6, 12, and 24 months (P < 0.001, P = 0.022, and P = 0.047, respectively). In a multivariable model (R2 = 0.576), the type of surgery was the only significant factor affecting postoperative BSCVA, in favor of DMEK (coefficient value -0.518, P = 0.002). CONCLUSIONS: In eyes with previous glaucoma surgery, DMEK and DSAEK had comparably low survival and comparably high rejection rates. Postoperative visual acuity might be better after DMEK in this setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".