MétaCan
Menu
Back to cohort
Record W4392594130 · doi:10.1080/17434440.2024.2327529

Navigating hypotony challenges with XEN gel implantation

2024· review· en· W4392594130 on OpenAlexaff
Khaled Ali Elubous

Bibliographic record

VenueExpert Review of Medical Devices · 2024
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsShuntingTube (container)MedicineOphthalmologySurgeryMaterials science

Abstract

fetched live from OpenAlex

INTRODUCTION: The XEN Gel, a hydrophilic tube meticulously crafted to adhere to the principles of the Hagen - Poiseuille law, is designed to facilitate efficient aqueous shunting without inducing hypotony. Implantable ab interno or ab externo, with or without conjunctival opening, the device shows no significant outcome differences. Despite numerical hypotony signaling failure, patients may fare well below 6 mmHg. AREAS COVERED: This review provides insights into device variability, challenges related to hypotony, associated risk factors, and hypotony management. EXPERT OPINION: The progressive evolution of the XEN Gel constitutes a significant advancement in the field of glaucoma management. Comparative studies investigating diverse implantation methodologies, particularly the ab interno and closed conjunctival approaches, highlight the device versatility in addressing individual patient needs. Exploring hypotony from both statistical and clinical perspectives challenges the traditional view of intraocular pressure as a straightforward success or failure indicator. The incidence of hypotony-related issues varies between device models, emphasizes the need for an individualized approach during device selection. Overall, understanding the dynamics of hypotony is crucial for optimizing the outcomes of XEN Gel implantation.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.045
GPT teacher head0.426
Teacher spread0.380 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of Medical DevicesSame topicGlaucoma and retinal disordersFrench-language works237,207