Good Intracranial Collaterals Trump Poor ASPECTS (Alberta Stroke Program Early CT Score) for Intravenous Thrombolysis in Anterior Circulation Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In acute ischemic stroke, large early infarct size estimated by the Alberta Stroke Program Early CT Score (ASPECTS) is associated with poorer outcomes and is a relative contraindication for recanalization therapies. The state of the intracranial collateral circulation influences the functional outcome and may be a variable to consider before thrombolysis. We evaluated the prognostic effect of the collateral circulation in patients with thrombolyzed acute ischemic stroke who have large early infarct sizes as indicated by low ASPECTS. MATERIALS AND METHODS: Patients with anterior circulation acute ischemic stroke who received a computed tomographic angiogram and subsequent treatment with intravenous tissue-type plasminogen activator from 2010 to 2013 were studied. Two independent neuroradiologists determined their ASPECTS. We stratified patients using ASPECTS into 2 groups: large volume infarcts (ASPECTS≤7 points) and small volume infarcts (ASPECTS 8-10). In addition, we evaluated a third group with very large volume infarcts (ASPECTS≤5 points). We then analyzed the 3 subgroups using the Maas, Tan, and ASPECTS-collaterals grading systems of the computed tomographic angiogram intracranial collaterals. Good outcomes were defined by modified Rankin Scale score of 0 to 2 at 3 months. RESULTS: A total of 300 patients were included in the final analysis. For patients with very large volume infarcts (ASPECTS≤5 points), univariable analysis showed that younger age, male sex, lower National Institute of Health Stroke Scale (NIHSS), lower systolic blood pressure, and good collaterals by Maas, Tan, or ASPECTS-collaterals grading were predictors of good outcomes. On multivariate analysis, younger age (odds ratio, 0.93; 95% confidence interval, 0.89-0.97; P=0.002) and good collaterals by ASPECTS-collaterals system (odds ratio, 1.34; 95% confidence interval, 1.15-1.57; P<0.001) were associated with good outcomes. CONCLUSIONS: In patients with large and very large volume infarcts, good collaterals as measured by the ASPECTS-collaterals system is associated with improved outcomes and can help select patients for intravenous thrombolysis.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".