Non‐invasive vascular function measurement tools: the correlation between arterial distensibility and venous compliance in young males (546.8)
Bibliographic record
Abstract
Reliable non‐invasive tools for the determination of arterial and venous health permit repeated assessments in intervention studies. While arterial health is commonly estimated using tonometry and venous function estimated using venous plethysmography, no studies have reported on the relation of these measures of vascular function. We assessed the reproducibility (ICC) and correlation (Pearson r) of Augmentation Index (AIx) and Venous Compliance (dV/dP) in a sample (n=8) of healthy (SBP/DBP: 107.1±7.4 / 70.0±5.5mmHg), young (Age: 25.5±3.7 years), physically active (Rapid Assessment of Physical Activity: 6.6±0.5) men. All participants completed an initial familiarization session, followed by two experimental sessions where both arterial Augmentation Index and Venous Compliance were measured. AIx values were accepted only if the operator index > 90%. Participants had good but varied arterial health (AIx = ‐1.9±11.0; range ‐18 to 17) and venous health (.03±.00; range .02 to .05). Reliability between days for AIx was high (Cronbach’s α= .97) while dV/dP scores at 20mmHg were less reliable (Cronbach’s α =.46). There was no correlation between intra‐individual same day AIx and dV/dP variables (r = ‐.31, p >0.05). Based on these results, studies of vascular function should consider the arterial health measurement of Augmentation Index and the venous function measurement of Venous Compliance as independent variables. Grant Funding Source : Supported by CIHR CGS Doctoral Research Award
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".