Directional sensitivity of the cerebral pressure-flow relationship during forced oscillations induced by oscillatory lower body negative pressure
Bibliographic record
Abstract
A directional sensitivity of the cerebral pressure-flow relationship has been described using repeated squat-stands. Oscillatory lower body negative pressure (OLBNP) is a reproducible method to characterize dynamic cerebral autoregulation (dCA). It could represent a safer method to examine the directional sensitivity of the cerebral pressure-flow relationship within clinical populations and/or during pharmaceutical administration. Therefore, examining the cerebral pressure-flow directional sensitivity during an OLBNP-induced cyclic physiological stress is crucial. We calculated changes in middle cerebral artery mean blood velocity (MCAv) per alterations to mean arterial pressure (MAP) to compute ratios adjusted for time intervals (ΔMCAv T /ΔMAP T ) with respect to the minimum-to-maximum MCAv and MAP, for each OLBNP transition (0 to −90 Torr), during 0.05 Hz and 0.10 Hz OLBNP. We then compared averaged ΔMCAv T /ΔMAP T during OLBNP-induced MAP increases (INC) (ΔMCAv T /[Formula: see text]) and decreases (DEC) (ΔMCAv T /[Formula: see text]). Nineteen healthy participants [9 females; 30 ± 6 years] were included. There were no differences in ΔMCAv T /ΔMAP T between INC and DEC at 0.05 Hz. ΔMCAv T /[Formula: see text] (1.06 ± 0.35 vs. 1.33 ± 0.60 cm⋅s −1 /mmHg; p = 0.0076) was lower than ΔMCAv T /[Formula: see text] at 0.10 Hz. These results support OLBNP as a model to evaluate the directional sensitivity of the cerebral pressure-flow relationship.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".