The CBV-ASPECT Score as a Predictor of Fatal Stroke in a Hyperacute State
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it