[Evidence and Patient Selection for Endovascular Reperfusion Therapy for Acute Ischemic Stroke:A Narrative Review].
Bibliographic record
Abstract
Three former stroke trials failed to show the efficacy of endovascular stroke reperfusion therapy using intra-arterial thrombolysis or older-generation mechanical thrombectomy devices, compared with usual medical care in 2013. However, five pivotal trials in 2015(MR CLEAN, ESCAPE, EXTEND-IA, SWIFT PRIME, and REVASCAT), using newer-generation devices(e.g., stent retrievers), have shown stroke thrombectomy to clearly improve the functional outcome of patients with occlusion of the internal carotid artery or the M1 portion of the middle cerebral artery(baseline National Institutes of Health Stroke Scale score ≥ 6; baseline Alberta Stroke Program Early Computed Tomography Score ≥ 6), and who could receive thrombectomy within 6 h of symptom onset . In 2018, the efficacy of stroke thrombectomy for late-presenting patients with up to 16-24 h of onset and those who had a mismatch between neurological severity and ischemic core volume was also established by the DAWN and DEFUSE 3 trials. In 2022, the efficacies of stroke thrombectomy for patients with a large ischemic core or basilar artery occlusion were identified. This article discusses the evidence and patient selection for endovascular reperfusion therapy for acute ischemic stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".