QUANTITATIVE FLUORESCEIN ANGIOGRAPHY BIOMARKERS IN DIABETIC MACULAR EDEMA
Bibliographic record
Abstract
PURPOSE: To investigate the association between baseline clinical characteristics of patients with diabetic macular edema (DME) and quantitative intravenous fluorescein angiography (IVFA) parameters. METHODS: Consecutive patients with DME presenting with a central macular thickness (CMT) ≥310 µ m were recruited from 2017 to 2023. Ultra-widefield IVFA images were analyzed with the RETICAD algorithm to extract quantitative measures of blood-retinal barrier (BRB) permeability, retinal perfusion, and blood flow. Univariable and multivariable regression models were used to investigate associations between IVFA parameters and baseline best-corrected visual acuity (BCVA), CMT, and macular volume. RESULTS: The study population consisted of 56 eyes with DME, and seven eyes from healthy controls. In our multivariable analysis, BRB permeability measured in the central and peripheral retina was significantly associated with BCVA ( P = 0.003 and 0.002, respectively) and macular volume ( P = 0.025 and 0.045, respectively). Both central and peripheral BRB permeability were significantly higher in DME eyes relative to healthy controls ( P < 0.001). CONCLUSION: Increased BRB permeability measured on IVFA in DME eyes was associated with a greater baseline macular volume and worse BCVA, suggesting its potential in providing an objective assessment of disease severity. Future research should explore the clinical utility of quantitative IVFA measurements in diverse patient populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".