Effect of postural changes on baroreflex sensitivity: a study on the EUROBAVAR data set
Bibliographic record
Abstract
Abstract: The effect of changing posture from supine to standing on the heart rate variability was analyzed on the EUROBAVAR data set using power spectral analysis. Power spectral analysis has been commonly used to provide indices of autonomic cardiovascular modulation. Although the interpretation of power spectra is in dispute, it is generally agreed that the high-frequency (HF) power components represent the cardiac vagal modulation and the low-frequency (LF) power components are related to sympathetic activity. And then their ratio (LF/HF) is considered as an index of sympathovagal balance. In the supine lying position, relatively fast vagal activity plays a dominant role, whereas the upright standing position results in vagal inhibition and sympathetic predominance. Postural changing from supine to standing position showed the significant decrease in the indices of vagal influence on HRV; HF power in both absolute and normalized unit decreased and LF/HF increased. The baroreflex sensitivity values were estimated by time sequence method, α-coefficients and transfer gains in LF and HF bands. There was a clear difference between BRS values in the supine and the standing positions with the averaged supine-to-standing BRS ratios of 2.35 ± 0.4; 2.47 ± 0.01 of HF BRS ratios and 1.84 ± 0.01 of LF BRS ratios, P < 0.001. Thus, the effect of changing positions to standing from lying can be concluded as a clear reduction in vagal influence on the heart rate variability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".