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Record W2312768038 · doi:10.1097/apo.0000000000000171

Nanotechnology Applications for Glaucoma

2015· review· en· W2312768038 on OpenAlexaff
Sibel Çetinel, Carlo Montemagno

Bibliographic record

VenueAsia-Pacific Journal of Ophthalmology · 2015
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsAlberta Glycomics CentreUniversity of Alberta
Fundersnot available
KeywordsGlaucomaBlindnessNanomedicineNanotechnologyIntraocular pressureMedicineOphthalmologyMaterials scienceOptometryNanoparticle

Abstract

fetched live from OpenAlex

Glaucoma is the second leading cause of blindness worldwide, and the antiglaucoma treatments currently available suffer from various complications. Nanotechnology-based treatments show a great deal of promise in overcoming these complications and form the basis for next-generation glaucoma treatment strategies, with the help of applications such as controlled release, targeted delivery, increased bioavailability, diffusion limitations, and biocompatibility. Significant progress has been made in nanomedicine in the efficiency of antiglaucoma medications, nanofabrication systems such as microelectromechanical systems that remove the limitations of nanodevices, and tissue regeneration vesicles for developing glaucoma treatments not based on intraocular pressure. With the use of these advanced technologies, the prevention of glaucoma-induced blindness will be possible in the near future. Herein, we reviewed the recent advances in nanotechnology-based treatment strategies for glaucoma.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.052
GPT teacher head0.369
Teacher spread0.317 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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