Successful reperfusion, rather than number of passes, predicts clinical outcome after mechanical thrombectomy
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".