Non‐invasive structural and metabolic retinal markers of disease activity in non‐proliferative diabetic retinopathy
Bibliographic record
Abstract
Abstract Purpose Metabolic and structural microvascular retinal alterations are essential components in diabetic retinopathy (DR). The present study aimed to measure changes at different stages of non‐proliferative DR (NPDR) and to explore interactions of imaging‐based metrics. Methods This cross‐sectional, cohort study included 139 eyes from 80 diabetic patients. Each patient underwent dilated fundal examinations including colour fundus photography, retinal oximetry and optical coherence tomography angiography (OCTA), analysed by semi‐automated and automated software. Diabetic retinopathy (DR) severity was classified according to the International Clinical Diabetic Retinopathy (ICDR) Severity Scale, ranging from no DR to severe NPDR (level 0–3). Retinal metabolism was evaluated by oximetry as retinal arteriolar (raSatO 2 ) and venular oxygen saturation (rvSatO 2 ), and macular microvascular structure was measured by OCTA as the area of foveal avascular zone (FAZ), vessel density (VD), vessel diameter index (VDI), FAZ circularity and fractal dimension (FD) in the superficial and deep retinal capillary plexus. Results A trend for increasing rvSatO 2 was found with increasing DR severity (51.3%, 53.3%, 54.2%, 59.8%, p = 0.02). Increasing severity of DR associated with decreasing FD in the superficial and deep plexus (p < 0.001 and p = 0.014), and in the superficial plexus decreasing VD (p < 0.001), increasing VDI (p = 0.003) and decreasing FAZ circularity (p = 0.006). A few interactions were identified between raSatO 2 , rvSatO 2 and VDI, but only in the deep capillary plexus (p < 0.01 and p < 0.01). Conclusion Alterations of the venular retinal vascular oxygen saturation and microvascular structural abnormities were found continuously throughout the DR‐spectrum. Given the sparse correlations between metabolic and structural abnormalities, it seems that these occur independently in DR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".