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Record W2298021796 · doi:10.1517/14712598.2016.1167868

Vascular endothelial growth factor inhibitor use and treatment approach for choroidal neovascularization secondary to pathologic myopia

2016· review· en· W2298021796 on OpenAlexaff
Kaivon Pakzad-Vaezi, Hemal Mehta, Zaid Mammo, Adnan Tufail

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2016
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAfliberceptRanibizumabMedicineMacular degenerationChoroidal neovascularizationOphthalmologyVascular endothelial growth factorBevacizumabSurgeryVEGF receptorsInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

INTRODUCTION: Myopic choroidal neovascularization (CNV) is the most common cause of CNV in those under 50 years of age. It is a significant cause of visual loss in those with pathologic myopia. The current standard of care involves therapy with intravitreal inhibitors of vascular endothelial growth factor (VEGF). AREAS COVERED: The epidemiology of myopia, high myopia, pathologic myopia, and myopic CNV is reviewed, along with a brief discussion of historical treatments. The pharmacology of the three most commonly used anti-VEGF agents is discussed, with an emphasis on the licensed drugs, ranibizumab and aflibercept. A comprehensive clinical approach to diagnosis and treatment of myopic CNV is presented. EXPERT OPINION: The current standard of care for myopic CNV is intravitreal inhibition of VEGF, with ranibizumab and aflibercept licensed for intraocular use. The diagnosis, OCT features of disease activity and retreatment algorithm for myopic CNV is different from wet age-related macular degeneration. In the long-term, myopic CNV may be associated with gradual, irreversible visual loss due to progressive chorioretinal atrophy, for which there is currently no treatment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.148
GPT teacher head0.370
Teacher spread0.222 · 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 teacher head, not a consensus.

Study designOther design
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

Citations3
Published2016
Admission routes1
Has abstractyes

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