Angiographic biomarkers are significant predictors of treatment response to intravitreal aflibercept in diabetic macular edema
Bibliographic record
Abstract
This prospective single-center study aims to identify biomarkers that predict improvement in best-corrected visual acuity (BCVA) and central retinal thickness (CRT) at 6 months, in 76 eyes with diabetic macular edema (DME) treated monthly with intravitreal aflibercept. At baseline, all patients underwent standardized imaging with color photography, optical coherence tomography (OCT), fluorescein angiography (FA) and OCT angiography (OCTA). Glycosylated hemoglobin, renal function, dyslipidemia, hypertension, cardiovascular disease and smoking were recorded. Retinal images were graded in a masked fashion. Baseline imaging, systemic and demographic variables were investigated to detect associations to BCVA and CRT change post aflibercept. Predictors of BCVA improvement included greater macular vessel density quantified using OCTA (p = 0.001) and low-density lipoprotein (LDL) ≥ 2.6 mmol/L (p = 0.017). Lower macular vessel density eyes showed a significant reduction in CRT but no BCVA improvement. Predictors of CRT reduction included peripheral non-perfusion seen on ultrawide-field FA (p = 0.005) and LDL ≥ 2.6 mmol/L (p < 0.001). Retinal angiographic biomarkers derived from OCTA and ultrawide-field FA may help predict functional and anatomic response to anti-vascular endothelial growth factor (VEGF) therapy in patients with DME. Elevated LDL is associated with treatment response in DME. These results may be used to better-select patients who will benefit from intravitreal aflibercept for treatment of DME.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".