GEOGRAPHIC ATROPHY INCIDENCE AND PROGRESSION AFTER INTRAVITREAL INJECTIONS OF ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR AGENTS FOR AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: Geographic atrophy (GA) is a complication of advanced neovascular age-related macular degeneration that can lead to permanent vision loss. We sought to estimate the incidence and progression of GA after intravitreal injections of antivascular endothelial growth factor agents in eyes with neovascular age-related macular degeneration. METHODS: Ovid MEDLINE, EMBASE, and Cochrane CENTRAL were searched from inception to May 2020. Included studies reported on the progression or development of GA in eyes with neovascular age-related macular degeneration after antivascular endothelial growth factor therapy. RESULTS: Thirty-one articles and 4,609 study eyes (4,501 patients) were included. Eyes received a mean of 17.7 injections over 35.2 months. The prevalence of GA at baseline was 9.7%. The pooled incidence of GA was 30.5% at the end of follow-up. There was a positive, moderate linear correlation between the mean total number of injections and GA incidence at the final follow-up (R2 = 0.30; P = 0.01). Monthly treatment was associated with a significantly higher risk for GA development relative to pro re nata (relative risk = 1.40, 95% confidence interval = [1.21-1.61], P < 0.001). Risk factors for GA development included GA in the fellow eye, retinal angiomatous proliferation, drusen, and reticular pseudodrusen. CONCLUSION: We found an association between the frequency and number of treatments with antivascular endothelial growth factor agents and the development of GA in neovascular age-related macular degeneration. Future studies should clarify risk factors, population characteristics, and relative contributions of treatment and disease progression on GA development in this context.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".