The CBV-ASPECT Score as a Predictor of Fatal Stroke in a Hyperacute State
Bibliographic record
Abstract
BACKGROUND: Many parameters of multimodal computed tomography (CT) have been assessed to predict clinical outcome and recanalization after thrombolysis. However, an early predictor of fatal stroke has not been clearly identified. Therefore, this study was conducted to identify early predictors related to fatal stroke. METHODS: We retrospectively analyzed subjects with acute ischemic stroke within 6 h of onset between March 2007 and January 2009. Early fatal stroke was defined as death or coma within 1 week of the initial ischemic stroke. Multimodal CT images were scored according to previous studies, such as the Alberta Stroke Program Early CT Score (ASPECTS), collateral score (CS) and clot burden score (CBS). RESULTS: A total of 68 patients were analyzed in this study. Twenty-two patients (32.4%) fell into a coma or died within 1 week of the initial stroke. Patients with fatal stroke had a lower CS, CBS and ASPECTS in the cerebral blood volume (CBV) and time-to-peak maps than those with nonfatal stroke. The initial NIHSS score, CBV-ASPECTS, age and diabetes mellitus were associated with fatal infarct in multivariate logistic regression analysis. CONCLUSIONS: Our study demonstrated that initially low CBV-ASPECTS on perfusion CT could predict early fatal stroke and that a CBV-ASPECTS threshold of <4 with a modest sensitivity and specificity could be considered as an early predictor of fatal 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".