Methodological considerations for cerebrovascular reactivity testing and analysis (1184.3)
Bibliographic record
Abstract
The cerebral microvasculature is exquisitely sensitive to carbon dioxide (CO 2 ) and this cerebrovascular reactivity (CVR) is often used as a measure of cerebrovascular function. However, a review of the current literature reveals that the details of CVR protocols and analyses vary considerably between research groups. The objective of this study is to determine the sensitivity of CVR to these methodologies. We used dynamic end‐tidal forcing to measure CVR to CO 2 under conditions of controlled normoxia, hypoxia and hyperoxia. Low levels of end‐tidal CO 2 were obtained by hyperventilation; high levels by hypoventilation with added inspired CO 2 . Supine blood pressure and middle cerebral artery blood flow velocity (MCAv) were continuously recorded, as were inspired and expired oxygen (O 2 ) and CO 2 . Here we discuss the physiological implications of different analysis techniques and also consider the effects on quantitative outcomes. We include the rationale and results of: different types of curve fitting; methods of normalizing MCAv; accounting for blood pressure changes; controlling O 2 ; and introducing a phase lag between end‐tidal CO 2 and MCAv. It is difficult to know whether CVR results are widely generalizable, or reflect unique protocols or analyses. This work is intended to stimulate discussion and encourage the publication of transparent methods in an effort to improve research reproducibility. Grant Funding Source : Supported by the Heart and Stroke Foundation of B.C. and Yukon (V.E.C.)
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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 | 0.453 | 0.481 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".