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Record W2763559849 · doi:10.1016/j.ajoc.2017.10.011

One-year outcomes of ziv-aflibercept for macular edema in central retinal vein occlusion

2017· article· en· W2763559849 on OpenAlexaff
Mohab Eldeeb, Errol W. Chan, Chintan Dedhia, Ahmad M. Mansour, Jay Chhablani

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsMcGill UniversityMontreal Clinical Research Institute
Fundersnot available
KeywordsMedicineAfliberceptCentral retinal vein occlusionMacular edemaOphthalmologyOcclusionRetinalEdemaBevacizumabSurgery

Abstract

fetched live from OpenAlex

PURPOSE: To report the 12-month efficacy and safety outcomes of intravitreal ziv-aflibercept in macular edema secondary to central retinal vein occlusion (CRVO). METHODS: Interventional case series documenting 12-month outcomes of intravitreal ziv-aflibercept (1.25 mg in 0.05 mL) in 6 patients with treatment-naive macular edema secondary to CRVO. All patients had comprehensive ophthalmic examination, spectral domain optical coherence tomography at baseline and all follow-up visits, and fluorescein. Retreatment decisions were based on recurrence or persistence of intraretinal or subretinal fluid, deterioration in visual acuity (VA), increase in central subfield thickness (CST) by ≥ 50 μm from the previous visit, or lowest recorded CST. RESULTS: . No eyes had uveitis, cataract progression, intraocular pressure (IOP) elevations, or systemic adverse events. CONCLUSIONS AND IMPORTANCE: Ziv-aflibercept achieves favorable intermediate-term functional and structural outcomes in macular edema secondary to CRVO. No safety concerns were raised. Low-cost ziv-aflibercept may thus be useful for CRVO in resource-poor countries. Further prospective studies in larger cohorts are needed further establish the efficacy and safety of this agent.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.352
Teacher spread0.326 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations13
Published2017
Admission routes1
Has abstractyes

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