Arterial Velocity Pulse Index as a Novel Marker of Atherosclerosis Using Pulse Wave Analysis on High Sensitivity Troponin T in Hypertensive Patients
Bibliographic record
Abstract
BACKGROUND: The arterial velocity pulse index (AVI) is explored as a novel marker of atherosclerosis using pulse wave analysis in clinical settings. Recent clinical studies have reported that the level of high-sensitivity troponin T (hs-cTnT) is an important biomarker in hypertensive patients. The aim of this study was to clarify the impact of AVI on hs-cTnT in these patients. METHODS: This study enrolled 455 hypertensive outpatients (181 males and 274 females; mean age, 65 ± 11 years (mean ± standard deviation)) without a history of cardiovascular events. AVI and hs-cTnT levels were measured using a commercial device, and relations among various clinical parameters, including AVI and hs-cTnT, were examined. RESULTS: Hs-cTnT was detected in 405 patients (89.0%). AVI was significantly higher in patients with detectable hs-cTnT than in those without (28 ± 7 vs. 24 ± 8, respectively, P < 0.001). In patients with detectable hs-cTnT, there was a significant positive correlation between AVI and hs-cTnT (r = 0.42, P < 0.001). Furthermore, multiple regression analyses revealed that AVI was an independent variable when hs-cTnT was used as a subordinate factor. On the other hand, hs-cTnT age, Cornell electrocardiographic voltage, height, urinary albumin excretion, pulse rate, and derivatives of reactive oxygen metabolites test were independent variables when AVI was used as a subordinate factor. CONCLUSION: The results of this study indicate that AVI reflects features of arterial wave reflection and is an important factor for hs-cTnT elevation in hypertensive patients.
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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 | 0.002 |
| 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 | 0.001 | 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".