Long-Term Outcomes of Descemet Membrane Endothelial Keratoplasty in Postvitrectomized Eyes With the Use of Pars Plana Infusion
Bibliographic record
Abstract
PURPOSE: To evaluate the use of pars plana infusion as part of Descemet membrane endothelial keratoplasty (DMEK) in eyes of patients who underwent vitrectomy. METHODS: A retrospective chart review was conducted of patients at Toronto Western Hospital (Toronto, Canada) who had undergone DMEK with pars plana infusion, with a minimum follow-up of at least 12 months. Collected data included postoperative best-corrected visual acuity (BCVA), intraoperative complications, and postoperative complications such as graft detachment, rejection and failure, and rate of endothelial cell loss. RESULTS: Fifteen eyes of 14 patients were included in this study. The mean follow-up time was 23.9 ± 5.7 months. Four grafts required rebubbling within the first month of surgery, and one graft required repeat DMEK right away. Two grafts failed secondarily at 24 months, and there was one episode of graft rejection. Five eyes had retinal complications including retinal detachment, retinoschisis, and cystoid macular edema. BCVA improved significantly from 1.7 ± 0.77 logarthim of the minimum angle of resolution (LogMAR) (mean Snellen 20/1000) preoperatively when compared with postoperative BCVA at 6 months (0.95 ± 0.74 LogMar, mean Snellen 20/180, P = 0.02, n = 10), 12 months (0.93 ± 0.6,P = 0.01, mean Snellen 20/170, n = 11), and 24 months (1.01 ± 0.68, mean Snellen 20/200 P = 0.046, n = 7). CONCLUSIONS: Although pars plana infusion is a helpful technique for DMEK in vitrectomized eyes, such cases are still quite difficult to perform compared with standard DMEK and use of an infusion may increase the risk of retinal complications. Descemet Stripping Automated Endothelial Keratoplasty may be the preferred technique in these challenging vitrectomized eyes.
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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 | 0.000 | 0.000 |
| 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".