Bibliographic record
Abstract
Abstract The term, minimally- or micro-invasive glaucoma surgery (MIGS), has entered common ophthalmic parlance and is playing an increasing role in the management of glaucoma patients. In common, the devices and procedures referred to are safer, less tissue invasive and associated with faster recovery than traditional filtering surgery, such as trabeculectomy or aqueous shunt implantation. MIGS can be categorized according to the tissue they target (or bypass): trabecular meshwork (TM) MIGS, subconjunctival MIGS, suprachoroidal MIGS and newer cycloablation procedures. A number of MIGS devices and techniques (e.g. TM MIGS) have relatively modest efficacy, but potential utility in a very large group of glaucoma patients with disease that is insufficiently severe to justify the invasiveness of conventional filtration surgery and the consequent intensity of postoperative care yet, burdened with medication and the attendant side effects and compliance issues thereof. On the other hand, subconjunctival MIGS devices, which are associated with bleb-related complications, can potentially achieve efficacy approaching that of traditional filtering surgery and are appropriate in selected individuals when larger IOP reductions are required, the exception being cases where glaucoma is very advanced. This book covers the techniques that are most commonly regarded as eligible to sit under the MIGS umbrella. Irrespective of the modest efficacy of many MIGS devices and techniques, the favourable safety profile lowers the threshold for early glaucoma surgery, especially when combined with cataract surgery, potentially delaying the requirement for more invasive surgery and associated risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.071 |
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".