Intravitreal Ranibizumab for the Treatment of Choroidal Neovascularization in Best’s Vitelliform Macular Dystrophy
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".