Impact of relative cerebral blood volume reduction on early neurological improvement in extensive ischemic stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The benefit of endovascular treatment (EVT) for patients with low Alberta Stroke Program early computed tomography score (ASPECTS) is still ambiguous and is currently being investigated in randomized trials. Computed tomography (CT) perfusion, used to estimate infarct extent and progression, might predict early neurological improvement (ENI) after EVT. We hypothesized that the degree of relative cerebral blood volume (rCBV) reduction is directly associated with ENI in low ASPECTS patients undergoing EVT. METHODS: Ischemic stroke patients with ASPECTS ≤ 5 who received multimodal CT and underwent thrombectomy were analyzed. rCBV reduction was defined as the ratio of cerebral blood volume (CBV), measured in the ischemic lesion to contralateral CBV. Complete reperfusion was defined as an expanded Thrombolysis in Cerebral Infarction score 2c-3. The clinical endpoint was ENI at 24 h, defined continuously (National Institutes of Health Stroke Scale [NIHSS] score change from baseline to 24 h) and binarized (NIHSS score at 24 h ≤ 8). RESULTS: A total of 102 patients were included. Lower rCBV reduction and complete EVT were independently associated with ENI (-11.4 NIHSS points, p = 0.04; -7.3 points, p < 0.0001, respectively). The effect of complete EVT on ENI was directly linked to the degree of rCBV reduction: the probability for binary ENI was +34.6% (p = 0.004) in patients with low rCBV reduction versus +8.2% (p = 0.28) in patients with high rCBV reduction). CONCLUSION: In patients with ischemic stroke with low ASPECTS, ENI was directly linked to the degree of rCBV reduction, a potential indicator of ischemia depth in extensive baseline infarction. Lower rCBV reduction was associated with higher probability of ENI after complete reperfusion, suggesting less pronounced lesion progression despite its large extent and hence, a higher susceptibility to EVT.
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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 | 0.001 | 0.003 |
| 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".