Assessment of human baroreflex function using carotid ultrasonography: what have we learnt?
Bibliographic record
Abstract
The arterial baroreflex is critical to both short- and long-term regulation of blood pressure. However, human baroreflex research has been largely limited to the association between blood pressure and cardiac period (or heart rate) or indices of vascular sympathetic function. Over the past decade, emerging techniques based on carotid ultrasound imaging have allowed new means of understanding and measuring the baroreflex. In this review, we describe the assessment of the mechanical and neural components of the baroreflex through the use of carotid ultrasound imaging. The mechanical component refers to the change in carotid artery diameter in response to changes in arterial pressure, and the neural component refers to the change in R-R interval (cardiac baroreflex) or muscle sympathetic nerve activity (sympathetic baroreflex) in response to this barosensory vessel stretch. The key analytical concepts and techniques are discussed, with a focus on the assessment of baroreflex sensitivity via the modified Oxford method. We illustrate how the application of carotid ultrasound imaging has contributed to a greater understanding of baroreflex physiology in humans, covering topics such as ageing and diurnal variation, and physiological challenges including exercise, postural changes and mental stress.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".