Effect of Faricimab versus Aflibercept on Hyperreflective Foci in Patients with Diabetic Macular Edema from the YOSEMITE/RHINE Trials
Bibliographic record
Abstract
Purpose: To compare the effect of faricimab, a dual angiopoietin-2 (Ang-2) and VEGF-A inhibitor, with aflibercept on resolution of hyperreflective foci (HRF) in patients with diabetic macular edema (DME). Design: A post hoc analysis of the randomized, double-masked, noninferiority YOSEMITE/RHINE (NCT03622580/NCT03622593) phase III trials. Participants: Adults with vision loss due to center-involving DME. Methods: A deep learning-based algorithm was used to automatically quantify HRF in spectral-domain OCT volume scans from YOSEMITE/RHINE. Study eyes were randomized to faricimab 6.0 mg every 8 weeks (Q8W; n = 519), faricimab 6.0 mg according to a personalized treat-and-extend (T&E)-based regimen (n = 524), and aflibercept 2.0 mg Q8W (n = 502). Hyperreflective foci were defined as hyperreflective objects up to 50 μm in diameter and assessed within the 1.0-mm and 3.0-mm-diameter ETDRS rings and by location within the inner and outer retina. Main Outcome Measures: Hyperreflective foci volume and count at baseline and over time through week 48 in the inner, outer, and total retina, 1-mm and 3-mm diameters; time to absence of HRF at 2 consecutive visits in the inner and outer retina, 1-mm diameter over 48 weeks. Results: < 0.001 for both). Similar results were obtained for volumes in the outer retina and for HRF counts. In the inner retina, 1-mm diameter, the 25th percentile for time to absence of HRF count at 2 consecutive visits was achieved 8 weeks earlier with faricimab Q8W and faricimab T&E versus aflibercept. Conclusions: Greater HRF reductions were achieved with faricimab versus aflibercept, supporting the therapeutic potential of dual Ang-2/VEGF-A inhibition to suppress disease activity in DME. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".