Successful reperfusion, rather than number of passes, predicts clinical outcome after mechanical thrombectomy
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: For patients undergoing mechanical thrombectomy, numerous (>3) thrombectomy passes may be harmful. However, non-recanalization leads to poor outcomes. For patients requiring multiple thrombectomy passes to achieve reperfusion, it remains unclear if the risk/benefit ratio favors recanalization. OBJECTIVE: To test the hypothesis that the benefits afforded by successful reperfusion outweigh the risk conveyed by the numerous passes required. METHODS: We retrospectively reviewed prospectively collected data for patients presenting to a comprehensive stroke center with anterior circulation large vessel occlusion (ACLVO) and undergoing thrombectomy requiring more than one pass over 24 months. We stratified patients into three groups: group 1 (successful reperfusion in 2-3 passes), group 2 (successful reperfusion in ≥4 passes), and group 3 (unsuccessful reperfusion). RESULTS: 250 patients with ACLVO constituted the study cohort. Despite similar demographics, group 2 patients had better clinical outcomes than those in group 3 at 24 hours (National Institutes of Health Stroke Scale (NIHSS) score 13.5 vs 19.1, p<0.001) and at 90 days (modified Rankin Scale score 0-2 rates of 31.1% vs 0.0%, p=0.006) On multivariate logistic regression analysis, age (p=0.034), Alberta Stroke Program Early CT Score (p<0.01), NIHSS score (p=0.02), and parenchymal hematoma type 2 (p=0.015) were significant predictors of functional independence among those who achieved successful reperfusion, but the number of passes required did not predict outcome for these patients (p=0.74). CONCLUSION: Patients who achieve successful reperfusion after many passes have better clinical outcomes than those who do not, despite the number of passes and procedural time required. The number of passes required to achieve successful reperfusion beyond the first pass is not a predictor of functional independence.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it