RADIAL-DIGITAL PULSE WAVE VELOCITY: A NON-INVASIVE METHOD FOR ASSESSING STIFFNESS OF PERIPHERAL SMALL ARTERIES
Bibliographic record
Abstract
Objective: Pulse wave velocity (PWV) is used to evaluate arterial stiffness of large and medium-sized arteries. Here, we examine the feasibility and reliability of radial-digital PWV (RD-PWV) as a measure of stiffness of smaller arteries, and its response to changes in hydrostatic pressure. Design and method: In 29 healthy subjects, we used Complior Analyse piezoelectric probes to record arterial pulse wave at radial artery and tip of the index. We determined transit time by second-derivative and intersecting-tangents using the device-embedded algorithms, in house Matlab-based analyses of only reliable waves, and by numerical simulation using a one dimensional (1-D) arterial tree model coupled with heart model. Results: Second-derivative RD-PWV were 4.68 ± 1.18, 4.69 ± 1.21, 4.32 ± 1.19 m/s for device-embedded, Matlab-based and numerical simulation analyses, respectively. Intersecting-tangents RD-PWV were 4.73 ± 1.20, 4.45 ± 1.08, 4.50 ± 0.84 m/s for device-embedded, Matlab-based and numerical simulation analyses, respectively. Intersession coefficients of variation were 7.0 ± 4.9% and 3.2 ± 1.9% (P = 0.04) for device-embedded and Matlab-based second derivative algorithms. In 15 subjects, we examine the response of RD-PWV to changes in local hydrostatic pressure by vertical displacement of the hand. For an increase of 10 mm Hg in local hydrostatic pressure RD-PWV increased by 0.28 m/s (95% CI: 0.16 to 0.40; P < 0.001). Conclusions: This study shows that RD-PWV can be used for the non-invasive assessment of stiffness of small-sized arteries. This finding allows for an integrated approach for assessing arterial stiffness gradient from aorta, to medium-sized arteries, and now to small-sized arteries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".