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Record W2787914477 · doi:10.18240/ijo.2018.01.21

A feasibility study of using biodegradable magnesium alloy in glaucoma drainage device

2018· review· en· W2787914477 on OpenAlexaff
Xiangji Li, Lin Xie, Fusheng Pan, Yong Wang, Hong Liu, Yurong Tang, Cindy Hutnik

Bibliographic record

VenueInternational Journal of Ophthalmology · 2018
Typereview
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsSt Joseph's Health Care
Fundersnot available
KeywordsMedicineGlaucomaIntensive care medicineMagnesiumSurgeryOphthalmologyMetallurgy

Abstract

fetched live from OpenAlex

Technological advances in glaucoma have challenged the traditional treatment paradigm. Historically incisional surgery has been used in cases of advanced disease and/or uncontrolled intraocular pressures resistant to medical or laser interventions. More recently, perhaps due to advancements in imaging, surgery has been suggested to be beneficial earlier in the treatment paradigm. Despite these trends, surgical manipulation of the tissues and unpredictability of wound healing continue to result in surgical failure. Magnesium is an essential element for human body and plays a critically important role in maintaining the functional and structural integrity of several tissues, including the eye. Due to several of its advantageous properties such as non-toxicity, biodegradability, and high biological compatibility, magnesium alloy has attracted great attention as a novel biomaterial. Biodegradable cardiovascular stents made of magnesium alloy have already been introduced into clinical practice. The purpose of this review is to determine if bioabsorbable magnesium alloys can be utilized as a promising candidate for the development of a new generation of glaucoma surgical assistive devices.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.142
GPT teacher head0.407
Teacher spread0.265 · 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 designBench or experimental
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

Citations16
Published2018
Admission routes1
Has abstractyes

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