Augmentation Index and Central Aortic Stiffness in Middle-Aged to Elderly Individuals
Bibliographic record
Abstract
BACKGROUND: Increased aortic stiffness contributes to systolic hypertension and increased cardiovascular risk. The augmentation index (AI), ie, the percentage of central pulse pressure attributed to reflected wave overlap in systole, was proposed as a noninvasive indicator of increased arterial stiffness. We evaluated this hypothesis by investigating relations between AI and other direct measures of aortic stiffness. METHODS: Tonometric carotid- and femoral-pressure waveforms, Doppler aortic flow, and aortic-root diameter were assessed in 123 individuals with uncomplicated systolic hypertension and 29 controls of comparable age and sex. Carotid-femoral pulse-wave velocity (PWV) was assessed from the carotid-femoral time delay and body-surface measurements. Aortic PWV was assessed from the ratio of the upstroke of carotid pressure and aortic flow velocity and was used to calculate proximal aortic compliance as [aortic area]/[1.06 x (aortic PWV)(2)]. RESULTS: Partial correlations (adjusted for age, sex, presence of hypertension, height, weight, and systolic ejection period) showed no association between AI and carotid-femoral PWV (R = -0.05, P = .54). The AI was significantly though weakly related directly with aortic compliance (R = 0.21, P = .012) and inversely with aortic PWV (R = -0.198, P = .017). However, higher stiffness (lower compliance and higher PWV) was associated with lower AI. CONCLUSIONS: Increased AI is not a reliable surrogate for increased aortic stiffness. Decreasing AI with decreasing compliance (increasing aortic stiffness) may be attributable to impedance matching and reduced wave reflection at the interface between the aorta and the muscular arteries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".