Abstract 3719: Large Vessel Correlates of Cerebral Small Vessel Disease
Bibliographic record
Abstract
Background: MRI-markers of ischemic cerebral small vessel disease (white matter hyperintensities [WMH] and lacunar infarcts) are common in the general population and are associated with an increased risk of stroke, dementia and mortality. The mechanisms underlying these lesions are poorly understood. Our aim was to investigate the relationship of carotid structure and function with MRI-markers of ischemic cerebral small vessel disease. Methods: The study population comprised 1847 community-based participants (mean age 72.5±4.1 years, 60.1% women), from the 3C-Dijon Study, who had undergone both quantitative brain MRI and carotid ultrasound examination. We used multivariable logistic and linear regression models adjusted for age, gender and vascular risk factors. Results: Presence of carotid plaques and increasing luminal carotid diameter were associated with a higher prevalence of lacunar infarcts (OR=1.68[95%CI:1.12-2.52], p=0.01 and OR=1.24[1.02-1.52], p=0.03 respectively), and extensive WMH volume (EXT-WMHV, OR=1.37[1.07-1.74], p=0.01 and OR=1.19[1.04-1.36], p=0.01, respectively), independently of vascular risk factors and of each other. There was a graded association of carotid plaques and luminal carotid diameter with increasing severity of ischemic cerebral small vessel disease. Increasing Young’s elastic modulus and carotid circumferential wall stress were associated with EXT-WMHV (OR=1.18[1.02-1.37], p=0.02 and OR=1.20[1.03-1.40], p=0.02 respectively), and there was a significant inverse association of carotid distension with WMH volume (beta±SE: -0.021±0.008, p=0.01), independent of vascular risk factors. Conclusion: Our results suggest that, in addition to the established association of carotid plaques with MRI-markers of ischemic brain injury, increasing luminal carotid diameter and markers of carotid stiffness, reflecting large artery arteriosclerosis, are associated with an increased prevalence of ischemic cerebral small vessel disease.
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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) | 0.001 | 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) | 0.008 | 0.001 |
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".