Assessment of cerebral autoregulation: the quandary of quantification
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Bibliographic record
Abstract
We assessed the convergent validity of commonly applied metrics of cerebral autoregulation (CA) to determine the extent to which the metrics can be used interchangeably. To examine between-subject relationships among low-frequency (LF; 0.07-0.2 Hz) and very-low-frequency (VLF; 0.02-0.07 Hz) transfer function coherence, phase, gain, and normalized gain, we performed retrospective transfer function analysis on spontaneous blood pressure and middle cerebral artery blood velocity recordings from 105 individuals. We characterized the relationships (n = 29) among spontaneous transfer function metrics and the rate of regulation index and autoregulatory index derived from bilateral thigh-cuff deflation tests. In addition, we analyzed data from subjects (n = 29) who underwent a repeated squat-to-stand protocol to determine the relationships between transfer function metrics during forced blood pressure fluctuations. Finally, data from subjects (n = 16) who underwent step changes in end-tidal P(CO2) (P(ET)(CO2) were analyzed to determine whether transfer function metrics could reliably track the modulation of CA within individuals. CA metrics were generally unrelated or showed only weak to moderate correlations. Changes in P(ET)(CO2) were positively related to coherence [LF: β = 0.0065 arbitrary units (AU)/mmHg and VLF: β = 0.011 AU/mmHg, both P < 0.01] and inversely related to phase (LF: β = -0.026 rad/mmHg and VLF: β = -0.018 rad/mmHg, both P < 0.01) and normalized gain (LF: β = -0.042%/mmHg(2) and VLF: β = -0.013%/mmHg(2), both P < 0.01). However, Pet(CO(2)) was positively associated with gain (LF: β = 0.0070 cm·s(-1)·mmHg(-2), P < 0.05; and VLF: β = 0.014 cm·s(-1)·mmHg(-2), P < 0.01). Thus, during changes in P(ET)(CO2), LF phase was inversely related to LF gain (β = -0.29 cm·s(-1)·mmHg(-1)·rad(-1), P < 0.01) but positively related to LF normalized gain (β = 1.3% mmHg(-1)/rad, P < 0.01). These findings collectively suggest that only select CA metrics can be used interchangeably and that interpretation of these measures should be done cautiously.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it