Abstract 107: The Effect of Baseline Reperfusion Status on Thrombectomy Outcomes in ICAS‐LVO — A Secondary Analysis of the RESCUE‐ICAS Registry
Bibliographic record
Abstract
Introduction Among patients with emergent large vessel occlusion due to intracranial stenosis (ICAS‐LVO), the effect of reperfusion status following thrombectomy remains unknown. In this secondary analysis of the RESCUE‐ICAS registry, we evaluated the impact of baseline reperfusion status on outcomes of mechanical thrombectomy with and without bailout stenting. Methods We conducted a secondary analysis of the RESCUE‐ICAS registry which included patients who underwent thrombectomy with or without bailout stenting for acute ICAS‐LVO. Patients were classified into four groups based on baseline reperfusion status and stenting use. Successful recanalization was defined as modified Thrombolysis in Cerebral Infarction (mTICI) ≥2B. The primary outcome was 90‐day functional independence (mRS 0‐2). Associations were analyzed using multivariable logistic regression with inverse probability of treatment weighting (IPTW). Results A total of 413 patients were analyzed across four groups: MT alone with failed recanalization (n=55), MT alone with successful recanalization (n=184), MT with stent after failed pre‐stent recanalization (n=108), and MT with stent after successful pre‐stent recanalization (n=66). Baseline characteristics were similar, except for age (p=0.004) and hyperlipidemia (p=0.003). On multivariable analysis, there was no difference in 90‐day outcome between patients with MT + bailout stenting with and without successful pre‐stent reperfusion. However, MT + bailout stenting with successful pre‐stenting reperfusion was associated with better 90‐day outcome compared to MT alone with successful reperfusion. Conclusion Adjunct stenting was associated with improved 90‐day functional outcomes in ICAS‐LVO patients even in patients with successful pre‐stenting reperfusion. These findings support early consideration of stenting in ICAS‐LVO to reduce re‐occlusion risk and improve long‐term outcomes. image
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".