Abstract TP189: Outcome Differences Between Endovascular Thrombectomy and Medical Management Based on Underlying Stroke Etiology: <i>A Secondary Analysis of SELECT2 Trial</i>
Bibliographic record
Abstract
Introduction: In patients with large core ischemic stroke, potential heterogeneity in EVT treatment effect based on underlying etiology is an important question. We explored differences in clinical outcomes and successful recanalization rates between EVT and MM. Methods: From SELECT2 randomized clinical trial, patients were categorized based on Stroke etiology: Large-artery atherosclerosis (LAA), Cardioembolism (CE), Stroke of other determined etiology (SOE), and Stroke of undetermined etiology (SUE). Procedure success, clinical outcomes and EVT treatment effect was compared based on stroke etiology. Results: Cardioembolic stroke was most frequently observed etiology across the trial (41%), followed by stroke of undetermined etiology (29%), large artery atherosclerosis (23%) and stroke of other determined etiology (6%). Proportion of patients achieving successful reperfusion (mTICI 2b-3) after EVT differed significantly across the categories (CE: 87%, LAA: 82%, SOE: 73%, SUE: 66%, p=0.040). However, treatment effect estimates favored EVT across categories of stroke etiology without significant heterogeneity - CE (ref): aGenOR: 1.83 (1.30-2.59) vs LAA: aGenOR: 2.04 (1.24-3.37), p-int: 0.87 vs SOE: aGenOR: 2.06 (0.80-5.28), p-int: 0.79 vs SUE: aGenOR: 1.20 (0.77-1.88), p-int: 0.17. Conclusion: In patients with large core ischemic stroke, proportion of patients achieving successful reperfusion differed based on stroke etiology. However, EVT was associated with better outcomes without evidence of significant heterogeneity. Further optimization of procedure techniques may help improve successful reperfusion rates and clinical outcomes in patients with SUE. Clinicaltrials.gov registration: NCT03876457
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".