Bibliographic record
Abstract
BACKGROUND: Aortic stiffness is a strong predictor of cardiovascular mortality in various clinical conditions. The aim of this review is to focus on the arterial stiffness gradient, to discuss the integrated role of medium-sized muscular conduit arteries in the regulation of pulsatile pressure and organ perfusion and to provide a rationale for integrating their mechanical properties into risk prediction. SUMMARY: The physiological arterial stiffness gradient results from a higher degree of vascular stiffness as the distance from the heart increases, creating multiple reflective sites and attenuating the pulsatile nature of the forward pressure wave along the arterial tree down to the microcirculation. The stiffness gradient hypothesis simultaneously explains its physiological beneficial effects from both cardiac and peripheral microcirculatory points of view. The loss or reversal of stiffness gradient leads to the transmission of a highly pulsatile pressure wave into the microcirculation. This suggests that a higher degree of stiffness of medium-sized conduit arteries may play a role in protecting the microcirculation from a highly pulsatile forward pressure wave. Using the ratio of carotid-femoral pulse wave velocity (PWV) to carotid-radial PWV, referred to as PWV ratio, a recent study in a dialysis cohort has shown that the PWV ratio is a better predictor of mortality than the classical carotid-femoral PWV. KEY MESSAGES: Theoretically, the use of the PWV ratio seems more logical for risk determination than aortic stiffness as it provides a better estimation of the loss of stiffness gradient, which is the unifying hypothesis that explains the impact of aortic stiffness both on the myocardium and on peripheral organs.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".