Aflibercept for Wet Age-Related Macular Degeneration: A Prospective, Randomized Trial Comparing Treat-And-Extend and Fixed Bimonthly Dosing
Bibliographic record
Abstract
Background/Objectives: Currently, treatments for age-related macular degeneration (AMD) consist of regular intravitreal injections that exert significant pressure on healthcare systems via their high labor costs and economic burden. To address this, our study’s goal is to propose new treatment protocols by comparing the efficacy of bimonthly fixed dosing aflibercept injections versus the treat-and-extend (T&E) approach for wet AMD. Secondary objectives included categorical best-corrected visual acuity (BCVA) changes, anatomical outcomes, treatment intervals, and adverse events. Methods: This study is a 1-year randomized, open-label, prospective trial that included 44 eyes from 44 patients, 32 in the T&E arm and 12 in the bimonthly arm. Treatment-naïve AMD patients with neovascularization were randomized to a bimonthly fixed dosing group or a T&E group after receiving 3 consecutive monthly aflibercept injections. Following the induction doses, retreatment intervals for patients in the T&E arm were adjusted based on a predetermined algorithm. Results: Over 12 months, mean BCVA improvements were 5.0 letters in the bimonthly group and 4.1 in the T&E group (p = 0.096 for non-inferiority test). On average, T&E patients received 9.6 injections compared to 7.7 for those in the fixed dosing group (p < 0.001). Anatomical outcomes were comparable in both treatment arms. Conclusions: In our trial, the T&E approach provided significant visual improvements, but did not meet the threshold for non-inferiority when compared to fixed bimonthly dosing. It was also unable to minimize treatment burden over the course of the first year of injections. Further research is required to optimize the T&E algorithm with aflibercept.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".