Ab Interno XEN Gel Stent Implantation in Eyes with Previous Tube Shunt Surgery
Bibliographic record
Abstract
Purpose: After tube shunt surgery, many factors may contribute to insufficient filtration over time, prompting further intervention to achieve optimal intraocular pressure (IOP) control. This study explores whether ab interno XEN gel stent implantation could be a viable approach in eyes that need further IOP reduction after tube shunt surgery. Methods: This is a retrospective, single-surgeon case series on ab interno XEN45 gel stent implantation in eyes that had previous tube shunt surgery. Main outcome measures were IOP and number of glaucoma medications at the pre-operative visit, post-operative week (POW) 1, and post-operative month (POM) 1, 3, 6, and 12. Adverse events and further interventions were noted. Surgery outcome was qualified as absolute success (IOP ≤ 18mmHg or ≥ 20% IOP reduction without glaucoma medications), qualified success (IOP ≤ 18mmHg or ≥ 20% IOP reduction with glaucoma medications), or failure (IOP > 18mmHg and < 20% IOP reduction with maximum tolerated glaucoma medications). Results: 7 eyes from 6 patients were included in this study. IOP was reduced from 23.9 ± 5.3 mmHg (mean ± standard deviation) pre-operatively to 14.0 ± 5.3 mmHg at POM12 (p < 0.05). Number of glaucoma medications was reduced from 4.3 ± 1.3 pre-operatively to 1.6 ± 1.6 at POM12 (p < 0.05). Hypotony and choroidal effusion were noted in one case which resolved before POM1. Bleb needling was required in 3 of the 7 eyes (43%), with one eye requiring needling twice. By POM12, 2 of 7 eyes (29%) achieved absolute success, 4 eyes (57%) qualified success, and 1 eye (14%) was qualified as failure. Conclusion: Ab interno XEN gel stent can effectively reduce IOP and number of glaucoma medications after failed tube shunt surgery. Nonetheless, further interventions such as bleb needling may still be required to optimize IOP control.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".