Cerebral Blood Volume ASPECTS Is the Best Predictor of Clinical Outcome in Acute Ischemic Stroke: A Retrospective, Combined Semi-Quantitative and Quantitative Assessment
Bibliographic record
Abstract
INTRODUCTION: The capability of CT perfusion (CTP) Alberta Stroke Program Early CT Score (ASPECTS) to predict outcome and identify ischemia severity in acute ischemic stroke (AIS) patients is still questioned. METHODS: 62 patients with AIS were imaged within 8 hours of symptom onset by non-contrast CT, CT angiography and CTP scans at admission and 24 hours. CTP ASPECTS was calculated on the affected hemisphere using cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) maps by subtracting 1 point for any abnormalities visually detected or measured within multiple cortical circular regions of interest according to previously established thresholds. MTT-CBV ASPECTS was considered as CTP ASPECTS mismatch. Hemorrhagic transformation (HT), recanalization status and reperfusion grade at 24 hours, final infarct volume at 7 days and modified Rankin scale (mRS) at 3 months after onset were recorded. RESULTS: Semi-quantitative and quantitative CTP ASPECTS were highly correlated (p<0.00001). CBF, CBV and MTT ASPECTS were higher in patients with no HT and mRS ≤ 2 and inversely associated with final infarct volume and mRS (p values: from p<0.05 to p<0.00001). CTP ASPECTS mismatch was slightly associated with radiological and clinical outcomes (p values: from p<0.05 to p<0.02) only if evaluated quantitatively. A CBV ASPECTS of 9 was the optimal semi-quantitative value for predicting outcome. CONCLUSIONS: Our findings suggest that visual inspection of CTP ASPECTS recognizes infarct and ischemic absolute values. Semi-quantitative CBV ASPECTS, but not CTP ASPECTS mismatch, represents a strong prognostic indicator, implying that core extent is the main determinant of outcome, irrespective of penumbra size.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".