Exploring pulse wave velocity as a vascular hemodynamic stress marker: more than just arterial stiffening?
Bibliographic record
Abstract
Arterial pulse wave velocity (PWV), defined as the speed at which a blood pressure pulse propagates along the arterial tree, is the gold standard for assessment of arterial stiffness and can serve as an independent predictor of cardiovascular events, such as myocardial infarction, stroke, and heart failure. However, recent animal data suggest that pulse wave velocity measurements may not only assess arterial stiffness but also highly dynamic changes in local homeostasis and the delicate artery whole body interplay. This narrative review summarizes the major contributing factors to changes in pulse wave velocity and proposes novel classification into these factors as being either intrinsic or extrinsic to the vasculature. Intrinsic factors known to modulate pulse wave velocity include the elastin, collagen and calcium content of the arterial wall, smooth muscle tone, and endothelial cell function. In contrast, extrinsic factors include variables such as sex, and others that can fluctuate such as blood pressure, heart rate, metabolic health, and age. We highlight how increases in pulse wave velocity may be variable and oversimplified depictions of aortic stiffness and suggest that they are holistic measurements of vascular hemodynamic stress that also include the cumulative impact of mechanical forces, biochemical alterations, and structural and/or functional changes to the vasculature.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".