Outcomes of XEN Stent in Patients With Glaucoma and Previous Corneal Transplantation
Bibliographic record
Abstract
PRÉCIS: The XEN stent safely and effectively controls intraocular pressure in select patients with history of corneal transplantation. PURPOSE: Glaucoma is a common complication after corneal transplantation and can be difficult to manage in these patients. This study reports outcomes of XEN stent implantation in eyes with glaucoma in the setting of previous corneal transplantation. PATIENTS AND METHODS: Noncomparative retrospective case series including eyes with a history of corneal transplantation and subsequent XEN stent implantation by a single glaucoma surgeon in Surrey, British Columbia, between 2017 and 2022. The analysis included patient demographics, pre and postoperative intraocular pressure (IOP), pre and postoperative glaucoma medications, peri and postoperative complications and interventions, and incidence of repeat corneal transplantation and additional glaucoma procedures to control IOP. RESULTS: Fourteen eyes with previous cornea transplantation underwent XEN stent implantation. Mean age was 70.1 years (range: 47-85 y). Mean follow-up was 18.2 months (range: 1.5-52 mo). The most common glaucoma diagnosis was secondary open angle glaucoma (50.0%). There was a significant reduction in IOP and the number of glaucoma agents at all postoperative time points ( P < 0.05). IOP decreased from 32.7 ± 10.0 mm Hg at baseline to 12.5 ± 4.7 mm Hg at the most recent follow-up. Glaucoma agents decreased from 4.0 ± 0.7 to 0.4 ± 1.0. Two eyes required additional glaucoma surgery to control IOP, with an average time to reoperation of 7 weeks. Two eyes underwent repeat corneal transplantation, with an average time to reoperation of 23.5 months. CONCLUSIONS: In selected patients with previous corneal transplants and refractory glaucoma, the XEN stent was safely implanted and effectively reduced IOP in the short term.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".