Methodological considerations for the measurement of arterial stiffness using applanation tonometry
Bibliographic record
Abstract
INTRODUCTION: Accurate comparisons of carotid--femoral pulse wave velocity (cfPWV) within and across studies require standardized procedures. Guidelines suggest reporting the average of at least two cfPWV measurements; if the difference exceeds 0.5 m/s, a third measurement should be taken, and the median reported. Another method involves repeating measurements until two values are within 0.5 m/s. However, in many studies, duplicate measurements are averaged irrespective of the difference between readings. We evaluated the impact of these methods on the reported cfPWV value. METHODS: Measurements of cfPWV (SphygmoCor) from five studies included individuals spanning a wide age range, with or without comorbid conditions, and pregnant women. In participants with at least three high-quality measurements, differences between the median value (MED) and the average of the first two cfPWV measurements (AVG1) and the average of two cfPWV measurements within 0.5 m/s (AVG2) were evaluated using paired t-tests and Bland--Altman plots. RESULTS: Participants' mean age was 50 ± 14 years and BMI was 28.0 ± 5.5 kg/m2 (N = 306, 79% women). The overall mean difference was -0.10 m/s (95% CI 0.17 to -0.04) between MED and AVG1, and 0.11 m/s (95% CI 0.05--0.17) between MED and AVG2. The absolute difference exceeded 0.5 m/s in 34% (MED-AVG1) and 22% (MED-AVG2) of participants, and 1 m/s in 8% of participants (both MED-AVG1 and MED-AVG2). Scatter around the bias line increased with higher mean cfPWV values. CONCLUSION: Although the overall mean difference in cfPWV between protocols was not clinically relevant, large variation led to absolute differences exceeding 0.5 m/s in a large proportion of participants.
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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 | 0.463 | 0.618 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".