Current and Emerging Concepts in the Management of Neovascular Age-Related Macular Degeneration
Bibliographic record
Abstract
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly worldwide. The more severe form of the disease, known as neovascular AMD, is characterized by aberrant growth of blood vessels from the choroid into the subretinal space. This pathologic choroidal neovascularization can have drastic consequences, often seriously impairing vision in affected individuals. Current treatment approaches focus on combination therapies that include photodynamic therapy in conjunction with numerous forms of antiangiogenic or anti-inflammatory drug intervention. To date, however, no adequate treatment is available for the majority of affected individuals. The threat of a rapidly aging population provides the impetus for aggressive efforts to control the prevalence and progression of this disease. This review will outline the currently available pharmacotherapies, discussing the justification for their use as well as their shortcomings. Furthermore, drugs that are currently under investigation as monotherapies and adjuncts will be highlighted. The potential for alternate targets will also be examined, with a focus on the most promising candidates.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".