P.25 Radial-Digital Pulse Wave Velocity: A Non-Invasive Method for Assessing Stiffness of Peripheral Small Arteries
Bibliographic record
Abstract
Abstract Pulse wave velocity (PWV) has been used to evaluate arterial stiffness of large arteries. Here, we examine the feasibility of radialdigital PWV (RD-PWV) as a measure of stiffness of smaller arteries, and its response to changes in local mean arterial pressure. In 29 healthy subjects, we used Complior probes to record arterial pulse wave at radial artery and tip of the index. To determine transit time, we used both second derivative and intersecting tangents of the entire recordings using the device-embedded algorithms, in house Matlab analyses of only reliable waves, and by numerical simulation using arterial tree model. In 15 subjects, we examine the response of RD-PWV to changes in local MAP by vertical displacement of the hand above and below the mid-axillary line. Using second derivative, RD-PWV were 4.68 ± 1.18, 4.69 ± 1.21, 4.32 ± 1.19 m/s respectively for deviceembedded, Matlab-based and numerical simulation analyses, respectively. Using intersecting tangents RD-PWV were 4.73 ± 1.20, 4.45 ± 1.08, 4.50 ± 0.84 m/s, respectively for device-embedded, Matlab-based and numerical simulation analyses, respectively. The strongest correlation (r = 0.92) was seen between device-embedded and Matlab-based second derivatives. The 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. We estimated that each increase of 10 mm Hg in local MAP by vertical displacement of the hand resulted in an increase in RD-PWV of 0.28 m/s. This study shows that RD-PWV can be used for the non-invasive assessment of stiffness of 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".