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Minimally Invasive Glaucoma Surgery: A Critical Appraisal of the Literature

2020· review· en· W3087546788 on OpenAlexaff
David J. Mathew, Yvonne M. Buys

Bibliographic record

VenueAnnual Review of Vision Science · 2020
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGlaucomaGlaucoma surgeryTrabeculectomySchlemm's canalIntraocular pressureGonioscopyBleb (medicine)SurgeryTrabecular meshworkOphthalmology

Abstract

fetched live from OpenAlex

Micro- or minimally invasive glaucoma surgeries (MIGS) have been the latest addition to the glaucoma surgical treatment paradigm. This term refers not to a single surgery, but rather to a group of distinct procedures and devices that aim to decrease intraocular pressure. Broadly, MIGS can be categorized into surgeries that increase the trabecular outflow [Trabectome, iStent (first and second generations), Hydrus microstent, Kahook Dual Blade and gonioscopy-assisted transluminal trabeculotomy], surgeries that increase suprachoroidal outflow (Cypass microstent and iStent Supra), and conjunctival bleb-forming procedures (Xen gel stent and InnFocus microshunt). Compared to traditional glaucoma surgeries, such as trabeculectomy and glaucoma drainage device implantation (Ahmed, Baerveldt, and Molteno valves), MIGS are touted to have less severe complications and shorter surgical time. MIGS represent an evolving field, and the efficacy and complications of each procedure should be considered independently, giving more importance to high-quality and longer-term studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0110.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.023
GPT teacher head0.400
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations62
Published2020
Admission routes1
Has abstractyes

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