Static autoregulation in humans: a review and reanalysis
Bibliographic record
Abstract
INTRODUCTION: Cerebral autoregulation (CA) is a theoretical construct characterized by the relationship between mean arterial pressure (MAP) and cerebral blood flow (CBF). We performed a comprehensive literature search to provide an up-to-date review on the static relationship between MAP and CBF. METHODS: The results are based on 40 studies (49 individual experimental protocols) in healthy subjects between 18 and 65 years. Exclusion criteria were: a ΔMAP <5%, hypoxia/hyperoxia or hypo/hypercapnia, and unstable levels (<2 min stages). The partial pressure of arterial CO2 (PaCO2) was measured in a subset of the included studies (n=28); therefore, CBF was also adjusted to account for small changes in PaCO2. RESULTS: The linear regression coefficient between MAP and CBF (or velocity) of 0.82±0.77%ΔCBF/%ΔMAP during decreases in MAP (n=23 experiments) was significantly different than the relationship of 0.21±0.47%ΔCBF/%ΔMAP during increases (n=26 experiments; p<0.001). After correction for increases/decreases in PaCO2, the slopes were not significantly different: 0.64±1.16%ΔCBF/%ΔMAP (n=16) and 0.39±0.30%ΔCBF/%ΔMAP (n=12) for increased vs. decreased MAP changes, respectively (p=0.60). CONCLUSION: The autoregulatory ability of the cerebral circulation appears to be more active in buffering increases in MAP as compared to reductions in MAP. However, the statistical finding of hysteresis is lost following an attempt to correct for PaCO2.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".