Association of Optical Coherence Tomography Angiography Biomarkers with Fluorescein Angiography Retinal Inflammation Scores in Behcet’s Retinal Vasculitis
Bibliographic record
Abstract
PURPOSE: To investigate the association of macular vessel density (VD) and foveal avascular zone (FAZ) parameters with the severity of retinal inflammation in patients with Behcet's retinal vasculitis. METHODS: In this prospective study, consecutive patients with Behcet's uveitis who had fluorescein angiography (FA)-proven retinal vasculitis underwent concurrent OCTA and FA imaging. Severity of retinal inflammation was quantitatively evaluated using our previously devised FA scoring system. Fovea-centered 6 × 6 mm spectral-domain OCTA scans were acquired, and VD of superficial vascular complex (SVC), deep vascular complex (DVC) and retina were measured for different measurement zones based on the ETDRS grid. FAZ area and perimeter were also measured. RESULTS: > 0.05). There was a significant negative association between superficial, deep and retina VD in all measurement zones except the fovea, with FA scores. This negative association was stronger in the DVC. Accordingly, greater deep VD in parafovea (B = -0.93; 95% CI -1.34 to -0.53), perifovea (B = -0.70; 95% CI -1.06 to -0.34) and whole ETDRS grid (B = -0.80; 95% CI -1.18 to -0.41) were associated with lower FA retinal inflammation scores. CONCLUSION: Greater macular vessel density is associated with lower retinal inflammation in patients with Behcet's retinal vasculitis in non-ischemic stages. The deep vascular complex is more severely affected in these patients. OCTA biomarkers have shown promising results for detecting microvascular changes in retinal inflammation.
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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 | 0.001 | 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".