P623A decade of testing arterial stiffness parameters: key results and insights
Bibliographic record
Abstract
Introduction: The overall goal of our investigations was to prove that the new oscillometric device provides a reliabile and feasibile alternative to measuring arterial stiffness parameters noninvasively in the clinical practice. We used arterial stiffness parameters to identify preclinical organ damage, to determine cardiovascular (CV) risk in different diseases. We also followed changes of similar stiffness parameters due to age or physical exercise in young healthy volunteers. Methods: Serial measurements of stiffness parameters (aortic pulse wave velocity (PWVao), and aortic augmentation index (AIXao) were performed noninvasively, using Arteriograph. We first determined the reference values of PWVao and AIXao in a large (3374 subjects) healthy population aged between 3–18 years, to analyse the physiological background of the observed age-related changes. We also determined the effect of single-bout exercise on aortic stiffness parameters in 108 young male subjects (mean age: 14.2±3.4 years). We investigated relationship between increased arterial stiffness and asymptomatic carotid atherosclerosis (ACA) and also compared central (ao) and local carotid (car) stiffness parameters in 361 patients. Further, we investigated the prognostic information provided by oscillometrically measured PWVao in 4146 healthy subjects during a follow-up period that lasted 5.5 years on average. In the clinical practice we first established a novel method for the continuous, 24-hour ambulatory measurement of stiffness parameters and the applied it to patients with verified obsctructive sleep apnea syndrome (OSA).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".