BEVACIZUMAB (AVASTIN) AND RANIBIZUMAB (LUCENTIS) FOR CHOROIDAL NEOVASCULARIZATION IN MULTIFOCAL CHOROIDITIS
Bibliographic record
Abstract
BACKGROUND: Multifocal choroiditis (MFC) is an inflammatory condition, occasionally associated with choroidal neovascularization (CNV). Bevacizumab (Avastin) and ranibizumab (Lucentis) are therapies that target vascular endothelial growth factor. Bevacizumab and ranibizumab have been used successfully to treat CNV in age-related and myopic macular degeneration. PURPOSE: : To describe the treatment of MFC-associated CNV with intravitreal bevacizumab and/or ranibizumab. DESIGN: Retrospective interventional case series. PARTICIPANTS: Six eyes of five patients with MFC-associated CNV were treated with intravitreal bevacizumab and/or ranibizumab. MAIN OUTCOME MEASURES: Visual acuity at 1, 3, and 6 months after the initial injection. RESULTS: Previous therapies (number of eyes treated) included sub-Tenon's corticosteroids (2), intravitreal corticosteroids (1), photodynamic therapy (1), and thermal laser (1). The mean number (range) of antivascular endothelial growth factor injections per eye was 2.3 (1-6). The mean duration (range) of follow-up per patient was 41.5 (25-69) weeks. Five of six eyes improved to 20/30 acuity or better at 6 months. One eye suffered a subfoveal rip of the retinal pigment epithelium with 20/400 acuity. There was a qualitative decrease in clinical and angiographic evidence of CNV. CONCLUSIONS: Bevacizumab and ranibizumab were effective at improving visual acuity over 6 months in a small series of patients with MFC-associated CNV. Tears of the retinal pigment epithelium may occur after intravitreal antivascular endothelial growth factor therapy in MFC-associated CNV.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".