P2‐100: IMPACT OF HYPERTENSION ON INTRACRANIAL ARTERIAL COMPLIANCE IN A LATINO COHORT
Bibliographic record
Abstract
Hypertension contributes to structural and functional changes of the arterial walls, resulting in decreased elasticity and increased stiffness of the arteries. Current studies are focused on assessing arterial stiffness of aortic or peripheral arteries measured by pulse wave velocity. Recent evidence suggests that intracranial arterial stiffness may directly contribute to the pathologies of cognitive impairment. However, to date, very few methods are available for non-invasively assessing intracranial vascular compliance. Recently, a novel non-invasive MRI technique using dynamic arterial spin labeling (ASL) has been developed to assess intracranial vascular compliance. In this study, we aimed to investigate the association between hypertension and intracranial vascular compliance in a Latino cohort. 21 volunteers (16 female, 69±6 years) over the age of 60 were enrolled in this study from the Los Angles Latino Eye Study (LALES) cohort. All the participants were evaluated for hypertension based on their medical history by interview. The Montreal Cognitive Assessment (MoCA) was available on 8 participants. ECG gated dynamic ASL MRI images were collected on a Siemens 3T scanner from each participant, which were used for assessing the intracranial vascular compliance (VC). Brachial pulse pressure was measured before and after VC scans. Intracranial vascular compliance in both large arteries and small arteries/arterioles were calculated. Intracranial vascular compliance reduced in hypertensive subjects in both large (p=0.03) and small (p=0.07) arteries, as shown in Figure 1. A negative trend (p=0.09) was also observed between intracranial vascular compliance in small arteries and cognitive performance, although there was a very limited sample size.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".