Measuring methods of blood pressure and arterial stiffness. A new approach
Bibliographic record
Abstract
Carotid-femoral pulse wave velocity (c-f PWV) is considered to be one of the main factors affecting CV risk in the hypertensive population and has been included to the detailed screening of the hypertensive patients according to the ESH guidelines. Despite the large number of studies proving the correlation of blood pressure (BP) and c-f PWV as well as arterial stiffness, there are only few studies to show which BP measurement method can better predict arterial stiffness. Generally, these studies express the superiority of ABPM in the prediction of c-f PWV and early vascular aging (EVA). Furthermore, while guidelines recommend measuring c-f PWV in the hypertensive population, on the other hand it is difficult, time consuming and expensive to measure arterial stiffness in the everyday clinical practice, limiting its use only for experts. These limitations lead to the creation of a new score, the Early Vascular Aging Ambulatory score (EVAAs), which uses parameters from the ABPM and other cardiovascular risk factors in order to identify the possibility of each patient to have EVA. The score should be further studied for its accuracy in a larger population, as well for predicting hard end points in prospective studies.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".