CAROTID-FEMORAL PULSE WAVE VELOCITY VARIABILITY: BEAT-TO-BEAT ASSESSMENT
Bibliographic record
Abstract
Objective: Variability of carotid pulse wave velocity (CFPWV) measurements may be related to measurement errors, but also to physiological beat-to-beat variations in pulse transit time (TT). We aimed to 1) evaluate beat-to-beat variability of CFPWV on simultaneous non-invasive carotid and femoral waveforms without signal artefacts, and 2) explore its clinical and hemodynamic determinants. Design and method: In 44 adult patients (47±18 years; 50% men; 32% hypertensive, 27% with chronic kidney disease, 9% diabetic and 5% with cardiovascular disease), three 10 seconds-long acquisitions of carotid and femoral pressure waveforms were performed using Complior Analyse. Raw data of the three recordings were extracted, checked to be artefact-free, concatenated, and subjected to a custom 2nd derivative-based foot detection algorithm. Mean, beat-to-beat standard deviation (SD), and coefficient of variation (CV) of CFPWV (80% of direct distance) and heart rate were determined. Regression analysis was used to identify determinants of CV of CFPWV. Results: 44 ± 3 (mean ± SD) beats per individual were analysed, and the mean CFPWV was 7.7±2.6 m/s. The SD and CV of CFPWV were 1.2±0.8 m/s and 13.9±6.5%, respectively. In multivariable regression analysis, age (standardized ß = 0.470, p<0.001) and intra-individual SD of heart rate (ß = 0.430, p<0.001) explained 63% of changes in CV of CFPWV. Systolic/diastolic blood pressures were not significant determinants of CV of CFPWV. Conclusions: There is a variability in beat-to-beat pulse transit time that is not explained by poor signal quality, but by higher physiological variations of beat-to-beat transit time, which is explained by advancing age and beat-to-beat heart rate variability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".