INVESTIGATING MICROANGIOPATHY USING SWEPT-SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN PATIENTS WITH SUSAC SYNDROME
Bibliographic record
Abstract
PURPOSE: To determine whether optical coherence tomography angiography is of diagnostic utility for Susac syndrome (SuS) by quantifying microvascular retinal changes. METHODS: We enrolled 18 eyes of 9 healthy controls and 18 eyes of 9 patients with chronic SuS (12 had previous branch retinal artery occlusions and 6 were clinically unaffected). Images of the fovea were taken using an optical coherence tomography angiography system. Analysis included vessel density, fractal dimension, vessel diameter, and measurements of the foveal avascular zone (area, eccentricity, acircularity index, and axis ratio) in deep and superficial retinal layers. RESULTS: Skeleton density and inner ring vessel density were significantly lower in patients with SuS (skeleton density: Susac 0.11 ± 0.01 vs. controls 0.12 ± 0.01, P = 0.027. VD: SuS 0.39 ± 0.04 vs. controls 0.42 ± 0.02, P = 0.041). Eccentricity and axis ratio were significantly higher in patients with SuS (EC: Susac 0.61 ± 0.11, controls 0.51 ± 0.10, P = 0.003; axis ratio: Susac 1.57 ± 0.28, controls 1.39 ± 0.11, P = 0.005). SuS eyes (affected and unaffected) had poorer outcomes of the remaining vascular parameters compared with controls (P > 0.05). CONCLUSION: Optical coherence tomography angiography identified chronic microvascular changes in the eyes of patients with chronic SuS. Even clinically unaffected SuS eyes showed poorer vascular parameters. Although further research is needed, this noninvasive imaging modality seems to have the potential to serve as a valuable additive diagnostic tool.
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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 | 0.001 |
| 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".