Mapping Cerebrovascular Reactivity Using Blood Oxygen Level-Dependent MRI in Patients With Arterial Steno-occlusive Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Blood oxygen level-dependent MRI (BOLD MRI) of hypercapnia-induced changes in cerebral blood flow is an emerging technique for mapping cerebrovascular reactivity (CVR). BOLD MRI signal reflects cerebral blood flow, but also depends on cerebral blood volume, cerebral metabolic rate, arterial oxygenation, and hematocrit. The purpose of this study was to determine whether, in patients with stenoocclusive disease, the BOLD MRI signal response to hypercapnia is directly related to changes in cerebral blood flow. METHODS: Thirty-eight patients with stenoocclusive disease underwent mapping of CVR by both BOLD MRI and arterial spin labeling MRI. The latter technique was used as a reference standard for measurement of cerebral blood flow changes. RESULTS: Hemispheric CVR measured by BOLD MRI was significantly correlated with that measured by arterial spin labeling MRI for both gray matter (R=0.83, P<0.0001) and white matter (R=0.80, P<0.0001). Diagnostic accuracy (area under receiver operating characteristic curve) for BOLD MRI discrimination between normal and abnormal hemispheric CVR was 0.90 (95% CI=0.81 to 0.98; P<0.001) for gray matter and 0.82 (95% CI=0.70 to 0.94; P<0.001) for white matter. Regions of paradoxical CVR on BOLD MRI had a moderate predictive value (14 of 19 hemispheres) for spatially corresponding paradoxical CVR on arterial spin labeling MRI. Complete absence of paradoxical CVR on BOLD MRI had a high predictive value (31 of 31 hemispheres) for corresponding nonparadoxical CVR on arterial spin labeling MRI. CONCLUSIONS: Arterial spin labeling MRI confirms that, even in patients with stenoocclusive disease, the BOLD MRI signal response to hypercapnia predominantly reflects changes in cerebral blood flow.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".