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 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".