Bibliographic record
Abstract
PURPOSE OF REVIEW: There is an increasing interest and availability of micro-invasive glaucoma surgery (MIGS) procedures. It is important that this increase is supported by sound, peer-reviewed evidence. This article will define MIGS, review relevant publications in the period of annual review and discuss future directions. RECENT FINDINGS: The results of the pivotal trial comparing a trabecular micro-bypass stent (iStent, Glaukos Corporation, Laguna Hills, CA, USA) combined with phacoemulsification to phacoemulsification alone showed a significantly higher percentage of patients with unmedicated intraocular pressure (IOP) ≤ 21 mmHg, and a comparable safety profile. Initial results are published regarding a second-generation micro-bypass stent (iStent inject, Glaukos Corporation, Laguna Hills, CA, USA), a canalicular scaffold (Hydrus, Ivantis Inc., Irvine, CA, USA) and an ab interno suprachoroidal microstent (CyPass, Transcend Medical, Menlo Park, CA, USA), showing a decrease in mean postoperative IOP. Phaco-Trabectome (Ab interno trabeculectomy Trabectome, NeoMedix Inc., Tustin, CA, USA) was compared to phacotrabeculectomy and showed less IOP reduction, less postoperative complications, and a similar success rate. Similar success rates were found with the comparison of excimer laser trabeculostomy (ELT, AIDA, Glautec AG, Nurnberg, Germany) and selective laser trabeculoplasty. A number of publications review the importance of the location of implantable devices, intraoperative gonioscopy, cost-effectiveness and quality-of-life studies, and randomized clinical trials. SUMMARY: MIGS procedures offer reduction in IOP, decrease in dependence on glaucoma medications and an excellent safety profile. Their role within our glaucoma treatment algorithm continues to be clarified and differs from the role of more invasive glaucoma surgeries such as trabeculectomy or glaucoma drainage devices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".