Alberta Stroke Program Early CT Scale Evaluation of Multimodal Computed Tomography in Predicting Clinical Outcomes of Stroke Patients Treated With Aspiration Thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patient selection is crucial in the endovascular treatment of acute ischemic stroke patients. Baseline computed tomographic (CT) images, evaluated with the Alberta Stroke Program Early CT Scale (ASPECTS), are considered significant predictors of outcome. In this study, we evaluated CT images and perfusion parameters, analyzed with ASPECTS, as final outcome predictors after endovascular stroke treatment. METHODS: We analyzed a cohort of patients with acute ischemic stroke and endovascular treatment. Patients with an occlusion of the M1 segment and multimodal CT imaging were included. CT perfusion data were reconstructed using commercial software. Two experienced neuroradiologists separately reviewed and scored CT and CT perfusion images with the ASPECTS score. Parameters were compared between patients with poor and with favorable follow-up outcome. Significantly different variables were further analyzed by forward stepwise logistic regression. RESULTS: Fifty-one patients were included in our study. Baseline characteristics did not differ between patients with favorable and poor outcomes. No significant difference in recanalization status, the various times, or CT ASPECTS was demonstrated between these 2 groups. Significant differences were demonstrated for age (P=0.0049), cerebral blood volume ASPECTS (P=0.0007), and between cerebral blood volume and cerebral blood flow ASPECTS (P=0.0045). Cerebral blood volume ASPECTS>7 demonstrated the highest sensitivity and specificity for favorable outcome with 84% and 79%, respectively. CONCLUSIONS: CT perfusion parameters, evaluated with ASPECTS, are optimal predictors of outcome and are more sensitive and specific than CT ASPECTS in the prediction of favorable outcome. Use of these parameters in treatment decisions could reduce futile recanalizations.
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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 | 0.004 |
| 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".