Long-Term Visual Outcomes for a Treat-and-Extend Antivascular Endothelial Growth Factor Regimen in Eyes with Neovascular Age-Related Macular Degeneration: Up to Seven-Year Follow-Up
Bibliographic record
Abstract
Purpose. To report long-term visual and anatomical outcomes in eyes with neovascular age-related macular degeneration (nAMD) treated with a treat-and-extend regimen (TER) of intravitreal antivascular endothelial growth factor (anti-VEGF) injections in real-world settings.Methods. Retrospective cohort study of consecutive patients with nAMD treated with a TER of anti-VEGF intravitreal injections by a single retina specialist (GC). Patients with nAMD who had at least one year of follow-up were identified using an electronic database. Best-corrected visual acuity (BCVA), comprehensive ophthalmologic examination, and macular OCT were performed at each visit. Patients received a loading dose of three monthly intravitreal injections and then were treated according to a TER of bevacizumab, ranibizumab, and/or aflibercept. The number of injections, BCVA, and central retinal thickness (CRT) were evaluated during the follow-up period.Results. 180 eyes from 180 patients were included in the study. Mean age was 75 ± 9 (range: 51–96). Mean BCVA was 0.77 ± 0.64 LogMAR at baseline, 0.69 ± 0.58 LogMAR ( p=0.0057 ) after loading phase, 0.64 ± 0.55 LogMAR ( p=0.0001 ) after 6 months of TER, and 0.76 ± 0.71 LogMAR after 6 years of treatment (n = 32 at year 6). CRT decreased significantly after the loading phase ( p=0.0002 ). The mean number of intravitreal injections per year was 7.6 during the first three years of treatment and then decreased to 5.9 during year 4 to 7.Conclusions. This retrospective study of 180 nAMD patients treated with a TER of intravitreal anti-VEGF demonstrates an initial improvement of BCVA after loading phase, followed by long-term visual stabilization for at least six years. These results were obtained with a high number of injections, averaging close to six injections per year during long-term follow-up. In light of the natural evolution of nAMD, these data support the long-term efficacy of this treatment under real-world conditions of heterogeneity of patients and type of anti-VEGF used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".