Comparison of diurnal variation, anatomical location, and biological sex within spontaneous and driven dynamic cerebral autoregulation measures
Bibliographic record
Abstract
Presently, the literature describing the influence of diurnal variation on dynamic cerebral autoregulation (dCA) metrics is sparse. Additionally, there is little data with respect to dCA comparisons between anterior/posterior circulation beds and biological sexes using squat-stand maneuvers. Eight male and eight female participants (n = 16) performed 5 min of spontaneous upright rest and squat-stand maneuvers at 0.05 and 0.10 Hz across seven time points throughout the day. All testing sessions commenced at 8:00 a.m. each day and dCA parameters were quantified across the cardiac cycle (diastole, mean, and systole) using transcranial Doppler ultrasound to insonate cerebral blood velocity within the middle and posterior cerebral arteries (MCA, PCA). No cardiac cycle alternations were seen spontaneous (all p > .207) while a trend was noted in some driven (all p > .051) dCA metrics. Driven dCA produced much lower coefficient of variances (all <21%) compared with spontaneous (all <58%). Moreover, no sex differences were found within driven metrics (all p > .096). Between vessels, PCA absolute gain was reduced within all spontaneous and driven measures (all p < .014) whereas coherence, phase, and normalized gain were unchanged (all p > .099). There appears to be little influence of diurnal variation on dCA measures across the day (8:00 a.m. to 6:00 p.m.). Absolute gain was blunted in the PCA relative to the MCA and consistent with previous literature, driven methods demonstrated vastly improved reproducibility metrics compared to spontaneous methods. Finally, no dCA differences were found between biological sexes, demonstrating that males and females regulate in a harmonious manner, when females are tested within the early follicular phase of the menstrual cycle.
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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 | 0.002 |
| 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".