Enhancing Descemet Membrane Endothelial Keratoplasty in Postvitrectomy Eyes With the Use of Pars Plana Infusion
Bibliographic record
Abstract
PURPOSE: To present a modified surgical technique to perform Descemet membrane endothelial keratoplasty (DMEK) in previously vitrectomized eyes and to analyze its safety and efficacy. METHODS: A retrospective analysis of previously vitrectomized eyes that underwent DMEK at Toronto Western Hospital was performed. The modified DMEK technique that was used included placement of a posterior pars plana infusion to reduce fluctuations in the anterior chamber depth and its excessive deepening. RESULTS: Twelve eyes of 12 patients (5 females and 7 males) aged 65.3 ± 21.5 years were included. Mean best-corrected visual acuity improved significantly from 1.72 ± 0.62 logMAR (mean Snellen ∼20/1040) preoperatively to 1.01 ± 0.64 logMAR (mean Snellen ∼20/200) at 6 months postoperatively (P = 0.017). Mean donor endothelial cell density was 2658 ± 229 cells/mm preoperatively and 1732 ± 454 cells/mm at 6 months after the procedure (mean percentage cell loss of 31.8%) (P = 0.046). There were no significant intraoperative complications, and no graft failures. One eye had graft detachment, which resolved after 2 rebubbling procedures. One eye had retinal detachment, which was corrected surgically. CONCLUSIONS: The use of posterior pars plana infusion in previously vitrectomized eyes stabilizes the anterior segment during DMEK, allowing for performance of DMEK surgery, and can potentially reduce intraoperative and postoperative complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".