Modeling reflex changes in pulsatile mechanics emphasizes a role for vascular capacitance in forearm vasomotor control
Bibliographic record
Abstract
Although sympathetic activation generally elicits vasoconstriction in skeletal muscle, it has variable effects on forearm vasomotor control across individuals, at least based on measures of resistance (R) that reflect changes in vascular caliber. However, R neglects changes in vascular capacitance (C) which is a critical determinant of the oscillatory component of pulsatile flow. The purpose of this study was to determine whether capacitance (C) can provide an additional measure of reflex vascular control. A modified Windkessel model that compares the pressure and flow waveforms was used to quantify the effects of R and C. Concurrent measures of brachial artery pressure (Finometer) and mean blood velocity waveforms (Doppler ultrasound) were acquired during baseline, −40 mmHg lower‐body negative pressure (LBNP) and a cold pressor test (CPT) (n=5). LBNP increased heart rate (p<0.05) with no change in mean arterial pressure. CPT caused an increase in mean arterial pressure (p<0.05) with no change in heart rate. R increased in 4 out of 5 subjects during LBNP (p<0.05) and in 3 of 5 subjects during CPT (n.s.). Regardless of the change in R, C was decreased in all subjects during both LBNP (−32%) and CPT (−30%) (p<0.05, both tests). These early results suggest that C may be an extended measure of reflex changes in vasomotor control. Supported by Natural Sciences and Engineering Research Council of Canada and Canadian Institutes of Health Research.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".