Estimated versus measured aortic stiffness: implications of diabetes, chronic kidney disease, sex and height
Bibliographic record
Abstract
BACKGROUND: Aortic stiffness is measured by carotid-femoral pulse wave velocity (PWV), but it can also be estimated (ePWV) based on age and brachial mean arterial pressure (MAP). However, diabetes mellitus and/or chronic kidney disease (DM/CKD) may cause more pronounced damage to the arterial wall, changing the pressure and PWV relationship. Furthermore, sex and height could affect PWV through their relationship to the arterial diameter and path length. The aim of the present study was to quantify the extent to which DM/CKD, sex and height affect the validity of ePWV in predicting PWV. METHODS: This cross-sectional study evaluated PWV in adult participants at high risk of aortic stiffness, using Complior and the second derivative transit time algorithm (PWV 2nd ). PWV 2nd was converted into intersecting tangent PWV (PWV ITc ), and ePWV was calculated using the Reference Values for Arterial Stiffness Collaboration formulas. RESULTS: Among 825 patients (62% males), the mean age was 60 ± 17 years, 34% had diabetes mellitus, 69% had CKD, and 24% did not have DM/CKD. MAP, ePWV, PWV 2nd , and PWV ITc were, respectively, 96 ± 14 mmHg, 9.8 (8.1-11.8) m/s, 9.5 (7.8-11.9) m/s and 11.3 (8.8-15.9) m/s. There was a significant interaction between DM/CKD, sex, and the predictive value of ePWV. Increasing height lowered the intercept but did not affect the slope of the relationship between estimated and measured PWVs. CONCLUSION: These findings suggest that the current ePWV equations do not accurately predict PWV in patients with DM/CKD, and that sex and height should also be considered in the future ePWV equations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".