One year effectiveness study of intravitreal aflibercept in neovascular age‐related macular degeneration: a meta‐analysis
Bibliographic record
Abstract
The current body of evidence on the efficacy and safety of aflibercept for age-related macular degeneration (AMD) is steadily growing as large clinical trials and observational studies are continually completed. Our aim was to analyse 1-year visual acuity (VA) outcomes in response to aflibercept therapy and identify factors affecting treatment response using evidence generated from a pooled analysis of current studies. A literature review of multiple electronic databases (EMBASE, MEDLINE, MedMEME) revealed 12 studies meeting inclusion and exclusion criteria for statistical analysis. Treatment posology, baseline patient characteristics, study type, sample size and 12-month change in VA were pooled in a meta-analysis with VA change as the main outcome. Data were then stratified by study design and posology in subgroup analyses. A meta-regression was conducted to regress 12-month VA change against posology, baseline VA and age. Users of aflibercept experienced an overall increase of 7.37 letters (95% confidence interval: 6.27-8.48, p heterogeneity: <0.001) in VA at 12 months of follow-up. In subgroup analyses, mean VA change was higher for randomized control trials and cohorts following regular posology (>7 injections/year) compared to observational studies and irregular posology. The meta-regression showed larger VA gains with regular posology compared to an irregular posology, and decreased effect size as age increased. This meta-analysis strongly suggests improved VA outcomes at 12 months in patients with wet AMD for 2.0 mg aflibercept, comparable to but slightly lower than landmark trials. Increased injection frequency and younger age demonstrates a trend with improved outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| 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 teacher head, 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".