Quantitative Measurement of Cerebrovascular Reactivity by Blood Oxygen Level-Dependent MR Imaging in Patients with Intracranial Stenosis: Preoperative Cerebrovascular Reactivity Predicts the Effect of Extracranial-Intracranial Bypass Surgery
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: CVR is a measure of cerebral hemodynamic impairment. A recently validated technique quantifies CVR by using a precise CO(2) vasodilatory stimulus and BOLD MR imaging. Our aim was to determine whether preoperative CO(2) BOLD CVR predicts the hemodynamic effect of ECIC bypass surgery in patients with intracranial steno-occlusive disease. MATERIALS AND METHODS: Twenty-five patients undergoing ECIC bypass surgery for treatment of intracranial stenosis or occlusion were recruited. CVR was measured preoperatively and postoperatively and expressed as %ΔBOLD MR signal intensity per mm Hg ΔPetCO(2). Using normative data from healthy subjects, we stratified patients on the basis of preoperative CVR into 3 groups: normal CVR, reduced CVR, and negative (paradoxical) CVR. Wilcoxon 2-sample tests (2-sided, α = 0.05) were used to determine whether the 3 groups differed with respect to change in CVR following bypass surgery. RESULTS: The group with normal preoperative CVR demonstrated no significant change in CVR following bypass surgery (mean, 0.22% ± 0.05% to 0.22% ± 0.01%; P = .881). The group with reduced preoperative CVR demonstrated a significant improvement following bypass surgery (mean, 0.08% ± 0.05% to 0.21 ± 0.08%; P < .001), and the group with paradoxical preoperative CVR demonstrated the greatest improvement (mean change, -0.04% ± 0.03% to 0.27% ± 0.03%; P = .028). CONCLUSIONS: Preoperative measurement of CVR by using CO(2) BOLD MR imaging predicts the hemodynamic effect of ECIC bypass in patients with intracranial steno-occlusive disease. The technique is potentially useful for selecting patients for 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) | 0.001 | 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".