Cardiac Baroreflex Variability and Resetting during Sustained Mild Effort
Bibliographic record
Abstract
This exploratory study assessed the pattern of closed-loop baroreflex resetting using multi-logistic-curve analysis. Operating point gain and ranges of RR-interval (RRI) and systolic blood pressure (SBP) are derived to examine how these relate to sympathetic activation. Sustained low-intensity isometric handgrip exercise, with a period of post-exercise circulatory occlusion (PECO) provided a model to study baroreflex resetting because the progression towards fatigue at constant tension induces a continuous increase in volitional contribution to neuro-cardiovascular control. Continuous measurements of muscle sympathetic nerve activity (MSNA), blood pressure, and RRI were made simultaneously throughout the experimental session. Spontaneous sequence analysis was used to detect episodes of baroreflex “engagements”, but the results are examined with a view to the fundamental difference between experimental conditions that isolate the carotid sinus (open-loop) and intact physiological conditions (closed-loop). While baroreflex function under open-loop conditions can be described in terms of a single logistic curve, intact physiologic conditions require a family of logistic curves. The results suggest that the baroreflex is in a “floating” state whereby it is continuously resetting during the timeline of the experiment but with minute-by-minute average values that mimic the less complex step-wise resetting pattern reported under open-loop conditions. Furthermore, the results indicate that baroreflex function and resetting of the operating point gain is reflected not in terms of change in the values of blood pressure or RR-interval but in terms of change in the range of values of these variables prevailing under different experimental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".