Early Recanalization With Alteplase in Stroke Because of Large Vessel Occlusion in the ESCAPE Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Quantitating the effect of intravenous alteplase on the technical outcome of early recanalization of large vessel occlusions aids understanding. We report the prevalence of early recanalization in patients with stroke because of large vessel occlusion treated with and without intravenous alteplase and endovascular thrombectomy, and its association with clinical outcome. METHODS: Patients with acute ischemic stroke with large vessel occlusion from the ESCAPE trial (Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion With Emphasis on Minimizing CT to Recanalization Times Trial) were included in this post hoc analysis. Outcomes of interest were the prevalence of early recanalization (1) and good outcome (2), defined as modified Rankin Scale score of 0 to 2 at 90 days. RESULTS: Among 147 patients who did not receive endovascular thrombectomy, early recanalization occurred in 4/30 (13.3%) patients without and 48/117 (41.0%) patients with intravenous alteplase (adjusted risk ratios, 3.2 [95% CI, 1.2-8.1]). Good outcome was achieved by 34/116 (29.3%) of patients who received intravenous alteplase versus 10/29 (34.5%) who did not receive alteplase (adjusted risk ratios, 1.0 [95% CI, 0.6-1.5) and by 20/52 (38.5%) patients with versus 24/93 (25.8%) without early recanalization (adjusted risk ratios, 1.9 [95% CI, 1.2-2.9]). CONCLUSIONS: Early recanalization was confirmed as a strong predictor of good outcome in patients who did not undergo endovascular thrombectomy and was improved with intravenous alteplase, yet a majority of patients (59.0%) did not achieve early reperfusion. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01778335.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".