Low Cerebral Blood Volume Is Predictive of Diffusion Restriction Only in Hyperacute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion-weighted MRI (DWI) demonstrates ischemic tissue with high sensitivity. Although low cerebral blood volume (CBV) is also used as a marker for infarction, the quantitative relationship between diffusion abnormalities and CBV is unknown. We tested the hypothesis that CBV would decrease proportionally to the apparent diffusion coefficient in patients with acute stroke and thus could be used as a surrogate parameter for diffusion restriction. METHODS: Perfusion-weighted imaging and DWI was performed in 54 patients within 28 hours of symptom onset. Mean apparent diffusion coefficient, cerebral blood flow, and CBV were measured within DWI lesions and contralateral regions. RESULTS: Within DWI lesions, CBV (3.3 ± 1.9 mL/100 g) was significantly decreased relative to contralateral regions (4.1 ± 2.1 mL/100 g, P < 0.001). Relative CBV was not decreased in patients with evidence of early reperfusion (1.2 ± 0.5) or mild stroke (National Institutes of Health Stroke Scale < 4, 1.1 ± 0.6). Linear regression indicated that relative CBV was predictive of relative apparent diffusion coefficient only in patients imaged within 9 hours of symptom onset (R = 0.50, P = 0.02). Ischemic tissue volumes generated using a CBV threshold of the 50th percentile of normal tissue were correlated with DWI lesion volumes (R = 0.73, P < 0.001). The mean difference between the CBV threshold of the 50th percentile of normal tissue and DWI lesion volumes was 6.3 mL (95% limits of agreement, 0.1 to 12.6 mL). CONCLUSIONS: Decreases in relative CBV are predictive of diffusion abnormalities in ischemic stroke. The pattern of CBV changes varies with clinical severity and symptom duration. Ischemic tissue volumes comparable to DWI lesions can be generated using CBV thresholds, but the use of this method is limited in patients with minor stroke.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".