SUSPENDING TREATMENT OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION IN CASES OF FUTILITY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it