Assessment of arterial stiffness using applanation tonometry
Bibliographic record
Abstract
Augmentation index (AIx) and pulse wave velocity (PWV) assess functional and structural aspects of the vascular wall and are independent markers of cardiovascular morbidity and mortality. Like blood pressure, many factors, genetic, structural, and physiological, affect AIx and PWV. AIx and PWV can be assessed noninvasively using applanation tonometry. The technique is simple, but comes with a number of practical and technical limitations that have not been well documented and (or) explored. This review considers pulse wave analysis in the context of cardiovascular disease, and considers its limitations. Data are presented indicating that the placement of the probe is critical, and that the amplitude of the obtained signal is related to the variability in measurements. On a more theoretical note, issues are discussed regarding the applied transfer functions that are built in the devices to assess central AIx from peripheral waveforms. Altogether, PWV and its analysis are useful additions to the arsenal of parameters that can be used to assess vascular health and to estimate vascular risk. Yet, our analysis underscores the necessity for precise operating procedures, and calls for transparency regarding the applied transfer functions of commercial devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".