The effect of an acute increase in central blood volume on hypercapnia‐induced attenuation in dynamic cerebral autoregulation
Bibliographic record
Abstract
It is well established that hypercapnia attenuates dynamic cerebral autoregulation. The impact of central blood volume on cerebral blood flow is controversial. The purpose of the present study was to examine whether hypercapnia‐induced attenuation in dynamic cerebral autoregulation is modified by acute central hypervolemia. Nine young healthy subjects voluntarily participated in this study. In order to measure dynamic cerebral autoregulation during normocapnic and hypercapnic (5%) conditions, the change in middle cerebral artery mean blood flow velocity (MCA V mean ) was analyzed during acute hypotension caused by two methods: 1) thigh‐cuff occlusion release (without change in central blood volume) and 2) lower body negative pressure (‐50 mmHg, LBNP) release (with acute increase in central blood volume). As expected, hypercapnia decreased the rate of regulation, as an index of dynamic cerebral autoregulation (0.236±0.053 and 0.167±0.074 sec ‐1 , respectively, P=0.025). In contrast, this attenuation was disappeared during central hypervolemia (P=0.574). This phenomenon may be associated with hypervolemia‐induced attenuation of dynamic cerebral autoregulation (P=0.009). These findings suggest that an acute change in central blood volume modifies carbon dioxide‐induced changes in dynamic cerebral autoregulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".