E.09 Predictors of optimal endovascular therapy results among patients with acute ischemic stroke
Bibliographic record
Abstract
Background: Several studies have demonstrated the safety and efficacy of endovascular therapy for patients with acute ischemic stroke. However, patient, imaging and treatment factors associated with the optimal functional outcome require better definition. Methods: We pooled data from 8 randomized controlled trials (SYNTHESIS, MR RESCUE, IMS III, MR CLEAN, ESCAPE, EXTEND-IA, SWIFT-PRIME, and REVASCAT). We conducted subgroup and sensitivity analyses to evaluate predictors of optimal functional results (modified Rankin scale, mRS) at 90 days. Results: Meta-analysis of 8 trials including 2,423 patients yielded that endovascular therapy resulted in 44.6% functional independence (mRS 0-2) versus 31.8% in the usual care group (OR 1.71, 95% CI 1.18-2.49, P=0.005). This treatment effect was significantly greater among patients with confirmed angiographic imaging of proximal arterial occlusion (OR 2.24, 95% CI 1.72-2.90, P<0.001), in patients who received the combined therapy of intravenous tPA and endovascular intervention (OR 2.07, 95% CI 1.46-2.92, P<0.001), and when using stent retriever for mechanical thrombectomy (OR 2.39, 95% CI 1.88-3.04, P<0.001). Conclusions: The relative functional benefit associated with endovascular therapy among patients with acute ischemic stroke was increased when combined with intravenous tPA, with confirmed proximal arterial occlusion on angiographic imaging, and with use of stent retrievers for mechanical thrombectomy.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".