Postural influences on the mechanical and neural components of the cardiovagal baroreflex
Bibliographic record
Abstract
AIM: The ability to maintain arterial blood pressure when faced with a postural challenge has implications for the occurrence of syncope and falls. It has been suggested that posture-induced declines in the mechanical component of the baroreflex response drive reductions in cardiovagal baroreflex sensitivity associated with postural stress. However, these conclusions are largely based upon spontaneous methods of baroreflex assessment, the accuracy of which has been questioned. Therefore, the aim was to engage a partially open-loop approach to explore the influence of posture on the mechanical and neural components of the baroreflex. METHODS: In nine healthy participants, we measured continuous blood pressure, heart rate, RR interval and carotid artery diameter during supine and standing postures. The modified Oxford method was used to quantify baroreflex sensitivity. RESULTS: In response to falling pressures, baroreflex sensitivity was similar between postures (P = 0.798). In response to rising pressures, there was an attenuated (P = 0.042) baroreflex sensitivity (mean ± SE) in the standing position (-0.70 ± 0.11 beats min(-1) mmHg(-1)) compared with supine (-0.83 ± 0.06 beats min(-1) mmHg(-1)). This was explained by a diminished (P = 0.016) neural component whilst standing (-30.17 ± 4.16 beats min(-1) mm(-1)) compared with supine (-38.23 ± 3.31 beats min(-1) mm(-1)). These effects were consistent when baroreflex sensitivity was determined using RR interval. CONCLUSION: Cardiovagal baroreflex sensitivity in response to rising pressures is reduced in young individuals during postural stress. Our data suggest that the mechanical component is unaffected by standing, and the reduction in baroreflex sensitivity is driven by the neural component.
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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 | 0.002 |
| 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 | 0.001 |
| 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".