Association of Arterial Pressure Volume Index With the Presence of Significantly Stenosed Coronary Vessels
Bibliographic record
Abstract
BACKGROUND: A blood pressure (BP) monitoring system (PASESA(®)) can be used to easily analyze the characteristics of central and peripheral arteries during the measurement of brachial BP. METHODS: We enrolled 108 consecutive patients (M/F = 86/22, age 70 ± 10 years) who underwent coronary angiography (CAG) due to suspected coronary artery disease (CAD) in whom we could measure various parameters using PASESA(®) in addition to brachial-ankle pulse wave velocity (baPWV). The patients were divided into two groups: patients who did not have significantly stenosed coronary vessel disease (n = 33, non-SVD group) and those who had at least one significantly stenosed coronary vessel (n = 75, SVD group). The characteristics of central and peripheral arteries (arterial velocity pulse index (AVI) and arterial pressure volume index (API), respectively) and baPWV were measured. Estimated central BP (eCBP) was calculated from the data obtained from PASESA(®), and CBP was also measured simultaneously by invasive catheterization. RESULTS: API, but not AVI and baPWV, in the SVD group was significantly higher than that in the non-SVD group. Although eCBP was significantly associated with CBP, there was no difference in eCBP between the groups. There were significant associations among API, AVI and baPWV, albeit these associations were relatively weak. A multivariate logistic regression revealed that API and β-blocker were significant independent variables that were associated with the presence of significant coronary stenosis. The cut-off level of API that gave the greatest sensitivity and specificity for the presence of SVD was 24 units (sensitivity 0.636 and specificity 0.667). CONCLUSION: In conclusion, API, but not AVI or baPWV, is associated with the presence of significant coronary stenosis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".