Clinical and Visual Field Outcomes Following Minimally Invasive Glaucoma Surgery Combined with Cataract Surgery
Bibliographic record
Abstract
Purpose: To determine the change in Humphrey visual field and clinical parameters after minimally invasive glaucoma surgery combined with cataract surgery. Patients and Methods: Patients undergoing minimally invasive glaucoma surgery combined with cataract surgery in a multicenter retrospective case series between 2013 and 2021 with reliable preoperative and 12 to 18 month postoperative visual field measurements were included. Devices included iStent, XEN, and Hydrus. Clinical parameters were compared with a generalized linear model with generalized estimating equations between preoperative and postoperative visits including best corrected visual acuity, intraocular pressure, number of glaucoma medications and visual fields. Visual field metrics included mean deviation (MD), pattern standard deviation (PSD), visual field index (VFI), and Collaborative Initial Glaucoma Treatment Study (CIGTS) score of total deviation probability and pattern deviation probability. Results: Forty-four eyes from 39 patients were included. During the follow up period, visual acuity improved from 0.23± 0.17 to 0.10± 0.14 logMAR (mean ± standard deviation, p < 0.001), number of glaucoma medications was reduced from 2.68± 1.06 to 1.46± 1.32 ( p < 0.001), and intraocular pressure decreased from 17.08± 4.23 mmHg to 14.92± 3.13 mmHg ( p =0.003). Differences across devices were negligible. The only significant difference was a greater reduction in number of glaucoma medications in the XEN group ( p < 0.001). There were no significant changes in the global parameters of VFI, MD, PSD, or CIGTS. Conclusion: Overall, minimally invasive glaucoma surgery combined with cataract surgery appears to be effective at stabilizing visual field function, reducing intraocular pressure, reducing number of glaucoma medications, and improving visual acuity over a 12 to 18 month follow-up period across MIGS devices. Keywords: glaucoma, MIGS, visual field, cataract
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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 | 0.001 | 0.001 |
| 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".