Factors affecting the determination of cerebrovascular reactivity
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cerebrovascular reactivity (CVR), measures the ability of the cerebrovasculature to respond to vasoactive stimuli such as CO2. CVR is often expressed as the ratio of cerebral blood flow change to CO2 change. We examine several factors affecting this measurement: blood pressure, stimulus pattern, response analysis and subject position. METHODS: Step and ramp increases in CO2 were implemented in nine subjects, seated and supine. Middle cerebral artery blood flow velocity (MCAv), and mean arterial pressure (MAP) were determined breath-by-breath. Cerebrovascular conductance (MCAc) was estimated as MCAv/MAP. CVR was calculated from both the relative and absolute measures of MCAc and MCAv responses. RESULTS: MAP increased with CO2 in some subjects so that relative CVR calculated from conductance responses were less than those calculated from CVR calculated from velocity responses. CVR measured from step responses were affected by the response dynamics, and were less than those calculated from CVR measured from ramp responses. Subject position did not affect CVR. CONCLUSIONS: (1) MAP increases with CO2 and acts as a confounding factor for CVR measurement; (2) CVR depends on the stimulus pattern used; (3) CVR did not differ from the sitting versus supine in these experiments; (4) CVR calculated from absolute changes of MCAv was less than that calculated from relative changes.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".