Acute ischemic stroke patients with diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score ≤ 5 can benefit from endovascular treatment: a single-center experience and literature review
Bibliographic record
Abstract
PURPOSE: The recommendation strength of the guidelines for mechanical thrombectomy among patients with large pre-treatment core infarct is weak. We evaluated the safety and outcome of endovascular treatment for acute ischemic stroke with diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) ≤ 5. METHODS: Data on acute ischemic stroke patients with DWI-ASPECTS ≤ 5 who underwent endovascular treatment within 6 h, or presented an arterial spin labeling-DWI (ASL-DWI) mismatch within 12 h, at our center were retrospectively collected. We report the clinical characteristics and outcome of every patient, and review the relevant literature. RESULTS: Among the 19 patients who were enrolled, all experienced successful reperfusion, and 10 achieved a favorable outcome (modified Rankin scale (mRS) ≤ 2). Two patients presented with symptomatic intracranial hemorrhage (sICH); both of them had a poor outcome (mRS > 2). CONCLUSION: Acute ischemic stroke patients with large DWI lesions caused by large vessel occlusion can achieve a favorable clinical outcome with endovascular treatment if recanalization is performed within 6 h, or after 6 h in case of an ASL-DWI mismatch.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".