Aflibercept 2 mg for Neovascular Age-Related Macular Degeneration: XTEND at 3 Years
Bibliographic record
Abstract
Introduction: Long-term, global data evaluating intravitreal aflibercept (IVT-AFL) 2 mg for treatment of neovascular age-related macular degeneration (nAMD) in real-world practice are needed. This study investigated the long-term, real-world effectiveness and safety of IVT-AFL 2 mg in patients with nAMD. METHODS: XTEND was a multicenter, observational, prospective study. Enrollment was conducted between May 2019 and May 2020; the patient follow-up period was 36 months. Treatment-naïve patients were treated with IVT-AFL 2 mg (fixed dosing or treat-and-extend [T&E]) according to the local label; for the T&E regimen, treatment intervals could be extended according to the national label (either European Medicines Agency [EMA]-aligned or non-EMA-aligned). RESULTS: Overall, 1,483 patients across 17 countries were treated with IVT-AFL 2 mg; mean ± standard deviation (SD) age was 78.8 ± 8.5 years; 60.4% were female. Overall, 62.6% of patients completed the 36-month follow-up visit. The mean (95% confidence interval [CI]) changes in best-corrected visual acuity from baseline were +4.6 (3.7, 5.4), +2.3 (1.3, 3.3), and +0.9 (-0.2, 1.9) at months 12, 24, and 36, respectively. The mean (95% CI) changes in central subfield thickness from baseline were -106 (-114, -99) μm, -109 (-117, -102) μm, and -110 (-118, -103) μm at months 12, 24, and 36, respectively. The mean ± SD numbers of injections from baseline to months 12, 24, and 36 were 7.7 ± 2.7, 11.3 ± 5.3, and 13.7 ± 7.5, respectively. No new safety concerns were identified. CONCLUSION: This study demonstrates that improvements with IVT-AFL 2 mg were maintained through month 36, suggesting that long-term durability of vision is achievable in patients with treatment-naïve nAMD in real-world practice. .
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".