Impact of Diffusion-Weighted Imaging Alberta Stroke Program Early Computed Tomography Score on the Success of Endovascular Reperfusion Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In acute ischemic stroke patients treated by intravenous thrombolysis, a diffusion-weighted imaging (DWI) Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is an independent factor of functional outcomes. Our aim was to assess the impact of pretreatment DWI-ASPECTS on outcomes after endovascular therapy, with a specific emphasis on recanalization. METHODS: We analyzed data collected between April 2007 and March 2013 in a prospective clinical registry of acute ischemic stroke patients treated by endovascular approach. Every patient with a documented internal carotid artery or middle cerebral artery occlusion who underwent an acute DWI-MRI before treatment was eligible for this study. The primary outcome was a favorable outcome defined by modified Rankin Scale of 0 to 2 at 90 days. RESULTS: Two hundred ten patients were included and median DWI-ASPECTS was 7 (interquartile range, 4-8). DWI-ASPECTS≥5 was the optimal threshold to predict a favorable outcome (area under the curve=0.69; sensitivity, 90%; specificity, 38%). In a multivariate analysis including confounding variables, the adjusted odds ratio for favorable outcomes associated with a DWI-ASPECTS of ≥5 was 5.06 (95% confidence interval, 1.86-13.77; P=0.002). Nonetheless, the occurrence of a complete recanalization was associated with an increased rate of favorable outcomes in patients with DWI-ASPECTS under 5 (50% versus 3%, P<0.001). CONCLUSIONS: DWI-ASPECTS≥5 seems to be the optimal threshold to predict favorable outcomes among patients undergoing endovascular reperfusion within 6 hours. Selected patients with a DWI-ASPECTS of <5 may still benefit when a complete reperfusion is achieved.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".