Acute Intravenous–Intra-Arterial Revascularization Therapy for Severe Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Intravenous alteplase for acute ischemic stroke is least efficacious for patients with proximal large-artery occlusions and clinically severe strokes. Intra-arterial therapy has the theoretical advantage of establishing a neurovascular diagnosis and high symptomatic artery patency rate but the disadvantage of requiring extra time and technical expertise. A combination of these two approaches may provide the best chance of improving outcome in severe acute ischemic stroke. We sought to assess the safety and feasibility of this approach. METHODS: This was a prospective, open-label study. Sequential patients arriving to our center within 3 hours of stroke onset who were treated with intravenous alteplase were screened for possible additional intra-arterial therapy using noninvasive neuroimaging. Clinical measures and outcomes were recorded prospectively. RESULTS: A total of 861 patients with ischemic stroke were admitted to Calgary hospitals during the study period. Eight patients over 21 months underwent a combined intravenous-intra-arterial approach. Six received intra-arterial alteplase and 1 underwent intracranial angioplasty; in a final patient, technical aspects prevented intra-arterial therapy. Early neurovascular and/or neurometabolic imaging identified the location of occlusion and tissue-at-risk (DWI-PWI mismatch) in all 8 patients. Two patients had a poor outcome, 1 patient suffered a significant groin hematoma, and there were no instances of symptomatic intracerebral hemorrhage. CONCLUSIONS: Intravenous followed by intra-arterial therapy is a promising approach to the treatment of severe acute ischemic stroke. Early noninvasive neurovascular and neurometabolic imaging is very helpful in choosing candidates for this type of therapy. On-going monitoring of alteplase-treated patients may allow the opportunity to perform rescue intra-arterial therapy.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".