Second-Generation Trabecular Micro-Bypass (iStent inject) with Cataract Surgery in Eyes with Normal-Tension Glaucoma: One-Year Outcomes of a Multi-Centre Study
Bibliographic record
Abstract
PURPOSE: The efficacy and safety of the trabecular micro-bypass stents (iStent and iStent inject) have been well documented in various open-angle glaucoma subtypes. However, their outcomes remain understudied in normal-tension glaucoma (NTG). The present study aimed to assess the 1-year outcomes related to the implantation of two second-generation trabecular micro-bypass stents (iStent inject) concomitant with cataract surgery (CE-TMS), exclusively in eyes with NTG. METHODS: This multi-center, consecutive case series included eyes with cataract and normal-tension glaucoma that underwent CE-TMS to reduce intraocular pressure or glaucoma medication use. The 12-month efficacy measures included change in average intraocular pressure (IOP) and medication burden. Safety included change in best-corrected visual acuity, cup-to-disc ratio, visual field mean-deviation and retinal nerve fiber layer thickness. Intra- or postoperative adverse events were noted. RESULTS: A total of 62 eyes with mild-to-severe NTG and average preoperative IOP of 15.82 ± 2.94 mmHg on 1.50 ± 1.28 glaucoma medications were included. Postoperatively, IOP declined by 22% from 15.82 ± 2.94 mmHg to 12.32 ± 2.58 (p < 0.001), all eyes had IOP ≤ 18 mmHg (versus 74% preoperatively), and half had IOP ≤ 12 mmHg (versus 15% preoperatively). Medication burden decreased by 70% from 1.50 ± 1.28 to 0.45 ± 0.86 (p < 0.001), and 73% of the eyes were medication-free (versus 23% preoperatively). Safety was favorable, with no evidence of sight-threatening adverse events. CONCLUSION: Implantation of iStent inject (two second-generation trabecular micro-bypass stents) combined with cataract surgery is efficacious in reducing IOP and medication burden with a favorable safety profile in eyes with mild-to-severe NTG.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".