Abstract 84: Efficacy of Endovascular Therapy in Acute Ischemic Stroke by Alberta Stroke Program Early Computed Tomography Score and National Institutes of Health Stroke Scale-A Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
Introduction and Aims: The ANGEL-ASPECT trial demonstrated the effectiveness of endovascular therapy (EVT) in acute ischemic stroke (AIS) patients with large infarct. We did a secondary analysis to explore the efficacy of EVT according to Alberta Stroke Program Early Computed Tomography Score (ASPECT) and National Institutes of Health Stroke Scale (NIHSS). Methods: ANGEL-ASPECT trial was a randomized clinical trial involving patients with acute anterior-circulation large-vessel occlusion and an ASPECT score of 3-5 or an infarct-core volume of 70-100ml. Patients were randomly assigned to EVT or standard medical treatment (SMT) alone. We categorized patients based on the combination of ASPECT (≤3 or 4-5) and NIHSS (<20 or ≥20) into four groups: low ASPECT & low NIHSS; low ASPECT & high NIHSS; high ASPECT & low NIHSS and high ASPECT & high NIHSS. We assessed the efficacy of EVT using ordinal shift analysis of 90-day modified Rankin Scale (mRS). Results: Among 455 patients, the prevalence of the four groups were 190 (41.8%), 70 (15.4%), 157 (34.5%) and 38 (8.4%), respectively. A significant shift in the distribution of 90-days mRS toward better outcomes in favor of EVT was observed in patients with low ASPECT & low NIHSS (common OR [cOR], 2.25; 95% CI, 1.34-3.76; P=0.002), and high ASPECT & low NIHSS (cOR, 2.30; 95% CI, 1.31-4.05; P=0.004), but not in other two groups with high NIHSS (all P >0.05). Conclusions: EVT was associated with better outcomes in AIS patients with large infarct and mild-to-moderate stroke severity (NIHSS <20), but not in those with severe stroke severity (NIHSS ≥20). Stroke severity should be considered when planning EVT for AIS patients with large infarcts.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".