Treatment regimens for optimising outcomes in patients with neovascular age-related macular degeneration
Bibliographic record
Abstract
Practice patterns for neovascular age-related macular degeneration (nAMD) have evolved from the landmark registration trials of vascular endothelial growth factor (VEGF) inhibitors. Non-monthly regimens like treat-and-extend (T&E) have become popular due to their effectiveness in clinical practice. T&E regimens attempt to limit the burden of visits and treatments by allowing progressively longer treatment intervals, but in so doing, are potentially associated with the expense of treating quiescent disease. This is acceptable to many patients and their ophthalmologists but can still be problematic in the real-world. Recent studies have further refined the T&E approach by allowing for quicker and longer extension of treatment intervals when less severe disease is detected. With newer drugs offering increased durability, a shift to longer regular intervals may emerge as a new practice pattern for VEGF inhibitor therapy. This review aims to consolidate the current literature on the most effective treatment patterns and update treatment guidelines based on options that are now available. It also summarises new aspects of nAMD management that may help to further refine current 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".