Hydrus microstent implantation for surgical management of glaucoma: a review of design, efficacy and safety
Bibliographic record
Abstract
With the advent of microinvasive glaucoma surgery (MIGS), the spectrum of modalities available to manage patients with this chronic and heterogeneous condition has broadened. Despite its novelty however, there has been a rapid evolution in the development of a multitude of devices, each targeting a structure along the aqueous drainage pathway. A growing body of evidence has demonstrated IOP and medication burden reduction, and a more favorable safety profile with MIGS procedures in contrast to traditional incisional surgeries. Among the array of MIGS, the Hydrus® Microstent (Ivantis, Inc., Irvine, CA) is a recent FDA approved device, designed to bypass the trabecular meshwork and provide a scaffold for Schlemm's canal. The objective of this article is to review the Hydrus from conception to clinical use, and present data on its efficacy and safety to date. The available literature has shown promise, however inherent to all novel devices, only long-term monitoring will ensure sustained IOP control and an acceptable safety profile. Surgical advancements in glaucoma have revolutionized the field, and continued research and development will establish these approaches in clinical treatment algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".