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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".