Arterial stiffness, cognitive impairment and dementia: confounding factor or real risk?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large artery stiffness is a frequent condition that arises with ageing, and is accelerated by the presence of co-morbidities like hypertension, obesity and diabetes. Although epidemiological studies have indicated an association between arterial stiffness, cognitive impairment and dementia, the precise effects of stiff arteries on the brain remains obscure. This is because, in humans, arterial stiffness is often accompanied by other factors such as age, high blood pressure, atherosclerosis and inflammation, which could themselves damage the brain independently of stiffness. Therefore, the question remains: is arterial stiffness a true risk for cognitive decline? Or, is it a confounding factor? In this review, we provide an overview of arterial stiffness and its impact on brain function based on human and animal studies. We summarize the evidence linking arterial stiffness to cognitive dysfunction and dementia, and discuss the role of new animal models to better understand the mechanisms by which arterial stiffness affects the brain. We close with an overview of treatments to correct stiffness and discuss the challenges to translate them to real patient care. This article is part of the Special Issue "Vascular Dementia".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 it