Intravitreal Anti-VEGF Treatment of Myopic Choroidal Neovascularization
Bibliographic record
Abstract
Purpose: To determine whether the presenting clinical features of active myopic choroidal neovascularization (CNV), including the presence of fibrosis or atrophy, limit the ultimate visual acuity gains from intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy. Methods: A retrospective analysis of 42 eyes with new-onset subfoveal CNV was performed. Only patients without concurrent age-related macular degeneration and with a spherical equivalent of at least −6.0 diopters were included in the study. All eyes received either intravitreal ranibizumab or bevacizumab injections as the primary treatment on a pro re nata basis for 1 year. Changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT) were recorded. Results: The mean number of ranibizumab (18 eyes) or bevacizumab (24 eyes) injections was 4.7 ± 0.5 over a mean follow-up time of 12 ± 0.4 months. The mean age of the patients was 62 ± 2.0 years. Based on optical coherence tomography staging at the initiation of treatment for active CNV, 30 had no fibrosis or atrophy (group 1), 5 had fibrotic stage, and 7 had atrophic stage CNV (the latter combined to form group 2). The BCVA for group 1 improved significantly ( P < .02) but worsened for group 2 ( P < .38), a statistically significant difference ( P < .05). The CRT for group 1 also declined significantly more than for group 2 ( P < .014). Conclusion: The presence of fibrosis or atrophy in eyes with active myopic CNV at the initiation of anti-VEGF therapy was associated with limited anatomic outcomes and visual gain.
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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) | 0.000 | 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) | 0.000 | 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".