The optimal measure of microvascular function with velocity time integral for cardiovascular risk prediction
Bibliographic record
Abstract
Recent evidence suggests that microvascular function may be important in cardiovascular risk prediction. One measure of microvascular function is hyperaemic velocity time integral (VTI). We assessed whether the VTI of more than one beat of reactive hyperaemia would provide a stronger correlate to traditional cardiovascular risk factors using a subset of subjects from the Firefighters and Their Endothelium (FATE) study. Vascular function was assessed by measurement of hyperaemic blood velocity with high-resolution ultrasound of the brachial artery. We evaluated three measures in the current analysis: the VTI of the first beat, average VTI of 10 beats, and maximum VTI of 10 beats post-cuff release. A total of 399 male subjects (45.5 ± 10 years) were included in this analysis. Univariate correlations between the three end points and cardiovascular risk factors were calculated, and multivariable regression models constructed. Intra-observer variability was approximately equal for all VTI end points (coefficient of variation: first = 1.6%, average = 1.4%, maximum = 1.4%). Univariate correlations between VTI and cardiovascular risk factors were similar across all three end points. In multivariable analyses, there were no differences in the relationships between cardiovascular risk factors and the various VTI end points (R(2) from 0.090 to 0.102). Age, systolic blood pressure, and BMI were predictors of the three VTI end points (p < 0.05). In conclusion, the first beat of reactive hyperaemia remains the suitable measure of microvascular function.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".