Quantification of the cerebral pressure–flow relationship directional sensitivity: Reliability of shorter repeated squat–stand durations
Bibliographic record
Abstract
Abstract The magnitude of changes in middle cerebral artery mean blood velocity (MCAv) is attenuated when mean arterial pressure (MAP) increases compared with when MAP decreases. This directional sensitivity has been characterized using a time‐corrected ratio calculated on MCAv and MAP changes induced by repeated squat–stands (RSS) at 0.05 and 0.10 Hz for 300 s (∆MCAv T /∆MAP T ). Herein, we examined the reliability of the metric within reduced RSS durations. Ninety‐nine (25 females) healthy human participants (26 ± 5 years) performed 300 s RSS at 0.05 Hz (20 s cycles) and 0.10 Hz (10 s cycles) while MCAv and MAP were measured continuously. The ∆MCAv T /∆MAP T was calculated for each transition [increase (INC); decrease (DEC)] of MAP for 60, 120, 180, 240 and 300 s. A two‐way ANOVA was completed, and the absolute (Bland–Altman plot) and relative (coefficient of variation and intraclass correlation coefficient) reliability were calculated to compare shorter‐duration recordings with 300 s (reference standard). At 0.05 Hz, ∆MCAv T /∆MAP T was similar between INC and DEC and comparable to the 300 s reference from 120 s onwards. At 0.10 Hz, ∆MCAv T /∆MAP T was lower during INC ( p < 0.0001). Bland–Altman plots indicated that differences trended towards zero (greater agreement) with increasing duration. Averaged coefficients of variation were <10% from 120 s (0.05 Hz) and 60 s (0.10 Hz) onwards. All intraclass correlation coefficients were >0.90 for recordings of ≥180 s in both frequencies. Although the 300 s reference is optimal, RSS duration could be shortened to 180 s, if needed, to identify this hysteresis‐like pattern reliably using ∆MCAv T /∆MAP T .
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".