Abstract TP458: Is There “Crosstalk” Between Intracranial Arterial Pathologies and Small Vessel Disease??
Bibliographic record
Abstract
Background: Interactions between intracranial arterial pathologies (IAP) and cerebral small vessel disease (SVD) are an increasingly debated topic. Hypothesis: We analyzed associations between the type/severity of SVD and two IAPs, the intracranial arterial calcifications (ICAC) and intracranial stenosis (ICS) in intracerebral hemorrhage (ICH) patients. Methods: Consecutive ICH patients from a prospective cohort were included. Patients were divided into those meeting Boston criteria for cerebral amyloid angiopathy (CAA) and those with strictly deep hypertensive ICH consistent with hypertensive SVD (HTN-SVD). White matter hyperintensity volume (WMH) and microbleed count (MB) were quantitatively measured on MRI as markers of SVD severity. Head CT angiography was rated for presence of ICAC and for presence of >50% intracranial arterial stenosis (ICS). Associations of IAPs with the type of SVD (CAA vs HTN) as well as imaging markers of SVD severity were analyzed in multivariate models. We also explored the association between IAPs and presence of pre-ICH dementia. Results: The cohort included 253 CAA patients and 90 HTN-SVD. CAA patients were older (73.5 vs 64.8, p<0.001), demonstrating higher WMH (25ml vs 16ml, p<0.001) but lower prevalence of hypertension than HTN-ICH. In univariate comparisons between CAA and HTN-SVD, the presence of ICACs (74% vs 72%, p=0.7) and ICS (7% vs 7.8%, p=0.8) were not different. ICS was not related to the type of SVD in multivariate models either. Using multivariate logistic regression, HTN-SVD was independently associated with presence of ICAC (adjusted OR = 2.56 [95% CI 1.1-6.2, p=0.002), as well as older age, male gender and hypercholesterolemia. We found no association between IAPs and parenchymal markers of SVD severity (WMH and MB) (all p>0.2) and no association with presence of dementia before ICH (p>0.2). Conclusions: HTN-SVD is associated with increased ICAC in multivariate models, suggesting either shared risk factors or direct interactions between SVD and IAP. There is no association of intracranial large artery pathologies (ICAC, ICS) with parenchymal (WMH, MB) or clinical (dementia) consequences of cerebral small vessel diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".