Considerations for Adding Minimally/Microinvasive Glaucoma Surgery (MIGS) to a Planned Cataract Surgery
Bibliographic record
Abstract
Glaucoma is a progressive optic neuropathy defined by retinal ganglion cells loss and characteristic visual field loss. It is a leading cause of irreversible blindness and affects over 60 million people worldwide. Its prevalence is estimated to increase to 111.8 million by 2040. Intraocular pressure (IOP) is a major clinically modifiable risk factor for glaucoma. Thus, glaucoma therapy aims to reduce the IOP using medications, lasers (e.g., selective laser trabeculoplasty) or surgery. Historically, surgery has been reserved for advanced glaucoma and in cases with poorly controlled pressure despite medical and laser treatment. For decades, trabeculectomy and tube shunt devices have been the predominant surgical methods for lowering ocular pressure. However, these traditional surgeries are invasive requiring significant manipulation of ocular tissue and have significant post-operative complication rates. Many patients have fallen in the gap of needing more pressure lowering but not enough to justify a higher risk surgery. Fortunately, the landscape of glaucoma surgery has rapidly evolved over the past 20 years with the emergence of minimally/micro- invasive glaucoma surgery (MIGS). MIGS is often performed as an adjunct to cataract surgery. As such, there is minimal added long-term risk if the procedure is done in the same space as the already planned cataract surgery. This represents a large group of patients, some of whom would not have been considered as glaucoma surgical candidates in the past. The clinician is now faced with the question, “Should I add MIGS to the cataract surgery?” In this paper, we suggest a series of questions to ask about each case in order to help make a patient-centred decision.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".