Methodological comparison of active- and passive-driven oscillations in blood pressure; implications for the assessment of cerebral pressure-flow relationships
Bibliographic record
Abstract
We examined the between-day reproducibility of active (squat-stand maneuvers)- and passive [oscillatory lower-body negative pressure (OLBNP) maneuvers]-driven oscillations in blood pressure. These relationships were examined in both younger (n = 10; 25 ± 3 yr) and older (n = 9; 66 ± 4 yr) adults. Each testing protocol incorporated rest (5 min), followed by driven maneuvers at 0.05 (5 min) and 0.10 (5 min) Hz to increase blood-pressure variability and improve assessment of the pressure-flow dynamics using linear transfer function analysis. Beat-to-beat blood pressure, middle cerebral artery velocity, and end-tidal partial pressure of CO2 were monitored. The pressure-flow relationship was quantified in the very low (0.02-0.07 Hz) and low (0.07-0.20 Hz) frequencies (LF; spontaneous data) and at 0.05 and 0.10 Hz (driven maneuvers point estimates). Although there were no between-age differences, very few spontaneous and OLBNP transfer function metrics met the criteria for acceptable reproducibility, as reflected in a between-day, within-subject coefficient of variation (CoV) of <20%. Combined CoV data consist of LF coherence (15.1 ± 12.2%), LF gain (15.1 ± 12.2%), and LF normalized gain (18.5 ± 10.9%); OLBNP data consist of 0.05 (12.1 ± 15.%) and 0.10 (4.7 ± 7.8%) Hz coherence. In contrast, the squat-stand maneuvers revealed that all metrics (coherence: 0.6 ± 0.5 and 0.3 ± 0.5%; gain: 17.4 ± 12.3 and 12.7 ± 11.0%; normalized gain: 16.7 ± 10.9 and 15.7 ± 11.0%; and phase: 11.6 ± 10.2 and 17.3 ± 10.8%) at 0.05 and 0.10 Hz, respectively, were considered biologically acceptable for reproducibility. These findings have important implications for the reliable assessment and interpretation of cerebral pressure-flow dynamics in humans.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".