Imaging Biomarkers and Their Impact on Therapeutic Decision-Making in the Management of Neovascular Age-Related Macular Degeneration
Bibliographic record
Abstract
These recommendations, produced by a group of Canadian retina experts, have been developed to assist both retina specialists and general ophthalmologists in the management of vision-threatening neovascular age-related macular degeneration (nAMD). The recommendations are based on published evidence as well as collective experience and expertise in routine clinical practice. We provide an update on practice principles for optimal patient care, focusing on identified imaging biomarkers, in particular retinal fluid, as well as current and emerging therapeutic approaches. Algorithms for delivering high-quality care and improving long-term patient outcomes are provided, with an emphasis on timely and appropriate treatment to preserve and maintain vision. In the context of nAMD, increasing macular fluid or leakage on fluorescein angiography (FA) may indicate disease activity regardless of its location. Early elimination of intraretinal fluid (IRF) is of particular relevance as it is a prognostic indicator of worse visual outcomes. Robust referral pathways for second opinion and peer-to-peer consultations must be in place for cases not responding to intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".