Improved White Matter Cerebrovascular Reactivity after Revascularization in Patients with Steno-Occlusive Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: One feature that patients with steno-occlusive cerebrovascular disease have in common is the presence of white matter (WM) lesions on MRI. The purpose of this study was to evaluate the effect of direct surgical revascularization on impaired WM cerebrovascular reactivity in patients with steno-occlusive disease. MATERIALS AND METHODS: We recruited 35 patients with steno-occlusive disease, Moyamoya disease (n = 24), Moyamoya syndrome (n = 3), atherosclerosis (n = 6), vasculitis (n = 1), and idiopathic stenosis (n = 1), who underwent unilateral brain revascularization using a direct superficial temporal artery–to-MCA bypass (19 women; mean age, 45.8 ± 16.5 years). WM cerebrovascular reactivity was measured preoperatively and postoperatively using blood oxygen level–dependent (BOLD) MR imaging during iso-oxic hypercapnic changes in end-tidal carbon dioxide and was expressed as %Δ BOLD MR signal intensity per millimeter end-tidal partial pressure of CO2. RESULTS: WM cerebrovascular reactivity significantly improved after direct unilateral superficial temporal artery–to-middle cerebral artery (STA-MCA) bypass in the revascularized hemisphere in the MCA territory (mean ± SD, −0.0005 ± 0.053 to 0.053 ± 0.046 %BOLD/mm Hg; P < .0001) and in the anterior cerebral artery territory (mean, 0.0015 ± 0.059 to 0.021 ± 0.052 %BOLD/mm Hg; P = .005). There was no difference in WM cerebrovascular reactivity in the ipsilateral posterior cerebral artery territory nor in the vascular territories of the nonrevascularized hemisphere (P < .05). CONCLUSIONS: Cerebral revascularization surgery is an effective treatment for reversing preoperative cerebrovascular reactivity deficits in WM. In addition, direct–STA-MCA bypass may prevent recurrence of preoperative symptoms.
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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) | 0.001 | 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".