Cerebral blood flow, cerebrovascular reactivity and their influence on ventilatory sensitivity
Bibliographic record
Abstract
New Findings What is the topic of this review? Cerebrovascular reactivity to CO 2 , which is a principal factor in determining ventilatory responses to CO 2 through the role reactivity plays in determining cerebral extra‐ and intracellular pH. What advances does it highlight? Recent animal evidence suggests central chemoreceptor vasculature may demonstrate regionally heterogeneous cerebrovascular reactivity to CO 2 , potentially as a protective mechanism against excessive CO 2 washout from the central chemoreceptors, thereby allowing ventilation to reflect the systemic acid–base balance needs (respiratory changes in ) rather than solely the cerebral needs. Ventilation per se does not influence cerebrovascular reactivity independent of changes in . Abstract Alveolar ventilation and cerebral blood flow are both predominantly regulated by arterial blood gases, especially arterial , and so are intricately entwined. In this review, the fundamental mechanisms underlying cerebrovascular reactivity and central chemoreceptor control of breathing are covered. We discuss the interaction of cerebral blood flow and its reactivity with the control of ventilation and ventilatory responsiveness to changes in , as well as the lack of influence of ventilation itself on cerebrovascular reactivity. We briefly summarize the effects of arterial hypoxaemia on the relationship between ventilatory and cerebrovascular response to both and . We then highlight key methodological considerations regarding the interaction of reactivity and ventilatory sensitivity, including the following: regional heterogeneity of cerebrovascular reactivity; a pharmacological approach for the reduction of cerebral blood flow; reactivity assessment techniques; the influence of mean arterial blood pressure; and sex‐related differences. Finally, we discuss ventilatory and cerebrovascular control in the context of high altitude and congestive heart failure. Future research directions and pertinent questions of interest are highlighted throughout.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".