OUTCOMES OF ANTI-VEGF THERAPY IN CHOROIDAL NEOVASCULARIZATION AFTER MACULAR SURGERY
Bibliographic record
Abstract
PURPOSE: To report treatment outcomes of anti-vascular endothelial growth factor (VEGF) therapy in choroidal neovascularization (CNV) presenting after macular surgery. METHODS: Retrospective analysis of 7 eyes of 7 patients, who were diagnosed to have CNV after macular surgery and were treated with anti-vascular endothelial growth factor therapy. Collected data included demographic details; history of present illness; surgical procedure; and clinical examination including visual acuity at presentation and follow-up with imaging and management. Main outcome measures were resolution of CNV activity at the last follow-up. Secondary outcomes included change in visual acuity at final follow-up from baseline, number of injections, treatment free interval, and adverse events. RESULTS: Seven eyes of 7 patients (2 females and 5 males), which underwent macular surgery (4 macular hole repairs and 3 epiretinal membrane (ERM) removal), were included in this study. Two eyes had drusen at the time of surgery; however, five eyes had no preexisting conditions. Mean interval between surgery and CNV development was 21.07 ± 38.55 months (range, 2 months-9 years). All patients had undergone intravitreal anti-vascular endothelial growth factor injections (range, 2-15; mean number: 5.85) with one eye requiring additional photodynamic therapy (PDT) and focal laser. Visual acuity was unchanged with inactive CNV at the last visit in all eyes after anti-vascular endothelial growth factor therapy. The mean follow-up duration after the development of CNV was 35.5 months (range, 6.5 months-8 years). CONCLUSION: Choroidal neovascularization occurring after otherwise successful macular surgery is uncommon with unknown predisposing factors. This entity appears to have poor visual outcome with currently available anti-vascular endothelial growth factor therapy.
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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 | 0.001 | 0.003 |
| 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".