Role of aortic arch vascular mechanics in cardiovagal baroreflex sensitivity
Bibliographic record
Abstract
Cardiovagal baroreflex sensitivity (cvBRS) measures the efficiency of the cardiovagal baroreflex to modulate heart rate in response to increases or decreases in systolic blood pressure (SBP). Given that baroreceptors are located in the walls of the carotid sinuses (CS) and aortic arch (AA), the arterial mechanics of these sites are important contributors to cvBRS. However, the relative contribution of CS and AA mechanics to cvBRS remains unclear. This study employed sex differences as a model to test the hypothesis that differences in cvBRS between groups would be explained by the vascular mechanics of the AA but not the CS. Thirty-six young, healthy, normotensive individuals (18 females; 24 ± 2 yr) were recruited. cvBRS was measured using transfer function analysis of the low-frequency region (0.04-0.15 Hz). Ultrasonography was performed at the CS and AA to obtain arterial diameters for the measurement of distensibility. Local pulse pressure (PP) was taken at the CS using a hand-held tonometer, whereas AA PP was estimated using a transfer function of brachial PP. Both cvBRS (25 ± 11 vs. 19 ± 7 ms/mmHg, P = 0.04) and AA distensibility (16.5 ± 6.0 vs. 10.5 ± 3.8 mmHg(-1) × 10(-3), P = 0.02) were greater in females than males. Sex differences in cvBRS were eliminated after controlling for AA distensibility (P = 0.19). There were no sex differences in CS distensibility (5.32 ± 2.3 vs. 4.63 ± 1.3 mmHg(-1) × 10(-3), P = 0.32). The present data demonstrate that AA mechanics are an important contributor to differences in cvBRS.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".