MétaCan
Menu
Back to cohort
Record W2079523484 · doi:10.4021//jmc.v3i4.619

Intravitreal Ranibizumab for the Treatment of Choroidal Neovascularization in Best’s Vitelliform Macular Dystrophy

2012· article· en· W2079523484 on OpenAlexvenueno aff
Figen Batıoğlu, Emin Özmert, Elçin Süren, Sibel Demirel

Bibliographic record

VenueJournal of Medical Cases · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRanibizumabOphthalmologyChoroidal neovascularizationVisual acuityFluorescein angiographyMacular dystrophyFundus (uterus)Macular degenerationOptical coherence tomographyBevacizumabSurgeryChemotherapy

Abstract

fetched live from OpenAlex

To evaluate the results of intravitreal ranibizumab injection in a case with choroidal neovascularization (CNV) in Best’s vitelliform macular dystrophy with different imaging modalities. A 20-year-old male with the complaint of reduced vision in the right eye underwent complete ophthalmological examination including fluorescein angiography (FA), fundus autofluorescence (FAF) imaging. Macular scans were obtained with spectral optical coherence tomography (OCT). Best corrected visual acuity was counting fingers at 2 meters in the right eye. Fluorescein angiography and OCT confirmed the diagnosis of  CNV associated with Best’s vitelliform macular dystrophy. After 3 monthly injections of intravitreal ranibizumab, CNV regressed and macular edema disappeared. Visual acuity improved to 10 / 10. His condition remained stable for 3 years after treatment. We concluded that the intrav itreal ranibizumab may be a new approach for the therapy of CNV in Best’s vitelliform macular dystrophy.  However, a long-term follow-up is warranted. Different imaging modalities help us to understand the different aspects of macular involvement in this disease complicated with CNV. doi:10.4021/jmc619w

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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 designOther design
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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Medical CasesSame topicRetinal Development and DisordersFrench-language works237,207