Comparison of Cerebral Vascular Reactivity Measures Obtained Using Breath-Holding and CO <sub>2</sub> Inhalation
Bibliographic record
Abstract
Stimulation of cerebral vasculature using hypercapnia has been widely used to study cerebral vascular reactivity (CVR), which can be expressed as the quantitative change in cerebral blood flow (CBF) per mm Hg change in end-tidal partial pressure of CO2 (PETCO2). We investigate whether different respiratory manipulations, with arterial spin labeling used to measure CBF, lead to consistent measures of CVR. The approaches included: (1) an automated system delivering variable concentrations of inspired CO2 for prospective targeting of PETCO2, (2) administration of a fixed concentration of CO2 leading to subject-dependent changes in PETCO2, (3) a breath-hold (BH) paradigm with physiologic modeling of CO2 accumulation, and (4) a maneuver combining breath-hold and hyperventilation. When CVR was expressed as the percent change in CBF per mm Hg change in PETCO2, methods 1 to 3 gave consistent results. The CVR values using method 4 were significantly lower. When CVR was expressed in terms of the absolute change in CBF (mL/100 g per minute per mm Hg), greater discrepancies became apparent: methods 2 and 3 gave lower absolute CVR values compared with method 1, and the value obtained with method 4 was dramatically lower. Our findings indicate that care must be taken to ensure that CVR is measured over the linear range of the CBF-CO2 dose-response curve, avoiding hypocapnic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".