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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".