Macular radial peripapillary capillary: a potential optical coherence tomography angiography biomarker of cognitive impairment in patients with internal carotid artery stenosis
Bibliographic record
Abstract
Objective We investigated retinal and choroidal microvascular parameters as potential biomarkers for vascular cognitive impairment in patients with internal carotid artery stenosis (ICAS). Methods We enrolled 123 asymptomatic ICAS patients and categorized them into vascular mild cognitive impairment (VMCI) and vascular dementia (VaD) groups using the Montreal Cognitive Assessment. Optical coherence tomography angiography was used to evaluate vessel densities and perfusion areas in various retinal layers. Magnetic resonance imaging-based neuroimaging biomarkers for cerebral small vessel disease (CSVD) were also assessed. Least absolute shrinkage and selection operator logistic regression identified predictor variables, and receiver operating curve analysis assessed the ability of key parameters to distinguish between VMCI and VaD. Results Compared with VMCI patients, VaD patients had lower radial peripapillary capillary (RPC) perfusion area, higher CSVD burden score, and larger white matter hyperintensity volume (all p < 0.05). Receiver operating curve analysis revealed that the RPC perfusion area of the affected eye had superior discriminatory power for distinguishing VaD from VMCI compared with both the CSVD burden score ( Z = 1.99, p = 0.047) and white matter hyperintensity ( Z = 1.97, p = 0.049). The optimal cutoff value for the 0–1 mm macular RPC perfusion area was determined as 0.068 mm 2 . Conclusion The optical coherence tomography angiography-derived RPC perfusion area can effectively differentiate VaD from VMCI, suggesting its potential as a noninvasive diagnostic method to support clinical decision-making for ICAS patients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".