Efficacy and Adverse Event Profile of the iStent and iStent Inject Trabecular Micro-bypass for Open-angle Glaucoma: A Meta-analysis
Bibliographic record
Abstract
Aim: This meta-analysis explores the efficacy and adverse event profile of the iStent, an ab interno implant for the treatment of open-angle glaucoma. Methods:A systematic literature search of Ovid MEDLINE and EMBASE was used to identify peer-reviewed original studies that provided efficacy data on the first or second generation iStent for at least five eyes.Intraocular pressure (IOP) was the primary efficacy endpoint, while the number of medication classes was the secondary outcome.Weighted mean differences were reported for continuous endpoints, while a relative risk was computed for dichotomous variables. Review Results:The search revealed 545 results, of which 1767 eyes from 28 studies were included.The cohort age was 71.4 ± 5.4 years, and 44.9% of patients were male.There was a significantly greater IOP reduction after the use of two first-generation stents compared to one, irrespective of phacoemulsification status (p < 0.001).Additionally, there was a significantly greater IOP reduction following iStent alone relative to phaco-iStent for the first-generation iStent (p < 0.001) and the iStent inject (p < 0.001).For the first generation stent, combined phaco-iStent provided a greater level of IOP reduction (p < 0.001) and reduction in the number of medication classes relative to phacoemulsification alone (p < 0.001).In total, 22.5% of eyes that received iStent implantation sustained some type of adverse event.The most common adverse events were intraocular pressure elevation, stent blockage or obstruction, stent malposition and hyphema. Conclusion and Clinical Significance:Statistically significant differences in efficacy outcomes exist between different numbers of stents and the presence or absence of concurrent phacoemulsification.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".