Minimally Invasive Glaucoma Surgery: A Review of the Literature
Bibliographic record
Abstract
Minimally invasive glaucoma surgery (MIGS) has emerged as a novel approach in the glaucoma treatment spectrum, offering a range of diverse procedures and devices aimed at reducing intraocular pressure (IOP). MIGS can be broadly classified into several categories: those that enhance trabecular outflow (Trabectome, iStent, Hydrus Microstent, Kahook Dual Blade, high frequency deep sclerotomy, and gonioscopy-assisted transluminal trabeculotomy), those that augment suprachoroidal outflow (CyPass Microstent and iStent Supra), those that target Schlemm's canal (TRAB360 and the OMNI Surgical System, Streamline, and Ab Interno Canaloplasty), and conjunctival bleb-forming procedures (EX-PRESS Glaucoma Filtration Device, Xen Gel Stent and PreserFlo MicroShunt). MIGS is considered to have a shorter surgical time and fewer severe complications when compared to traditional glaucoma surgeries such as trabeculectomy and glaucoma drainage device implantation (Ahmed, Baerveldt, and Molteno valves). This literature review comprehensively examines the distinct MIGS devices and procedures, their underlying mechanisms, and clinical outcomes, emphasizing the importance of evaluating the efficacy and complications of each approach individually. As the field of MIGS continues to evolve, it is crucial to prioritize high-quality, long-term studies to better understand the safety and effectiveness of these innovative interventions in glaucoma management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".