SUSPENDING TREATMENT OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION IN CASES OF FUTILITY
Bibliographic record
Abstract
PURPOSE: To provide guidance on the management of patients with neovascular age-related macular degeneration and its subtypes who respond poorly to anti-vascular endothelial growth factor (anti-VEGF) therapy, and to identify cases where suspending anti-VEGF treatment may be warranted. METHODS: Through a literature review and the combined knowledge and clinical experience of retinal experts, the Steering Committee of the Bayer-sponsored Vision Academy developed an algorithm for determining when to suspend anti-VEGF treatment of neovascular age-related macular degeneration in cases of futility. RESULTS: Consideration of factors that may cause suboptimal response to anti-VEGF therapy, such as undertreatment or misdiagnosis of the underlying condition, and factors that may preclude continued treatment, such as injection- or drug-induced complications, is necessary for adjusting treatment protocols in patients who respond poorly to anti-VEGF. If poor response to treatment persists after switching to an alternative anti-VEGF agent and no change in response is observed after withholding treatment for a predetermined period of time ("treatment pause"), anti-VEGF treatment may be considered futile and should be suspended. CONCLUSION: This publication introduces an algorithm to guide the management of neovascular age-related macular degeneration in patients showing poor response to anti-VEGF treatment and provides expert guidance for suspending anti-VEGF treatment in cases of futility.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".