Preoperative and postoperative mapping of cerebrovascular reactivity in moyamoya disease by using blood oxygen level—dependent magnetic resonance imaging
Bibliographic record
Abstract
OBJECT: The ability to map cerebrovascular reactivity (CVR) at the tissue level in patients with moyamoya disease could have considerable impact on patient management, especially in guiding surgical intervention and assessing the effectiveness of surgical revascularization. This paper introduces a new noninvasive magnetic resonance (MR) imaging-based method to map CVR. Preoperative and postoperative results are reported in three cases to demonstrate the efficacy of this technique in assessing vascular reserve at the microvascular level. METHODS: Three patients with angiographically confirmed moyamoya disease were evaluated before and after surgical revascularization. Measurements of CVR were obtained by rapidly manipulating end-tidal PCO2 between hypercapnic and hypocapnic states during MR imaging. The CVR maps were then calculated by comparing the percentage of changes in MR signal with changes in end-tidal PCO2. Presurgical CVR maps showed distinct regions of positive and negative reactivity that correlated precisely with the vascular territories supplied by severely narrowed vessels. Postsurgical reactivity maps demonstrated improvement in the two patients with positive clinical outcome and no change in the patient in whom a failed superficial temporal artery-middle cerebral artery bypass was performed. CONCLUSIONS: Magnetic imaging-based CVR mapping during rapid manipulation of end-tidal PCO2 is an exciting new method for determining the location and extent of abnormal vascular reactivity secondary to proximal large-vessel stenoses in moyamoya disease. Although the study group is small, there seems to be considerable potential for guiding preoperative decisions and monitoring efficacy of surgical revascularization.
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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 | 0.001 |
| 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) | 0.000 | 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".