Outcomes of Rescue Endovascular Treatment of Emergent Large Vessel Occlusion in Patients With Underlying Intracranial Atherosclerosis: Insights From STAR
Bibliographic record
Abstract
Background Some emergent large vessel occlusions (ELVOs) are refractory to reperfusion because of underlying intracranial atherosclerosis (ICAS), often requiring rescue therapy (RT) with balloon angioplasty, stenting, or both. In this study, we investigate the safety, efficacy, and long‐term outcomes of RT in the setting of mechanical thrombectomy for ICAS‐related ELVO. Methods and Results We queried the databases of 10 thrombectomy‐capable centers in North America and Europe included in STAR (Stroke Thrombectomy and Aneurysm Registry). Patients with ELVO who underwent ICAS‐related RT were included. A matched sample was produced for variables of age, admission National Institute of Health Stroke Scale, Alberta Stroke Program Early CT Score, onset to groin puncture time, occlusion site, and final recanalization. Out of 3025 patients with MT, 182 (6%) patients required RT because of underlying ICAS. Balloon angioplasty was performed on 122 patients, and 117 patients had intracranial stenting. In the matched analysis, 141 patients who received RT matched to a similar number of controls. The number of thrombectomy passes was higher (3 versus 1, P <0.001), and procedural time was longer in the RT group (52 minutes versus 36 minutes, P =0.004). There was a higher rate of symptomatic hemorrhagic transformation in the RT group (7.8% versus 4.3%, P =0.211), however, the difference was not significant. There was no difference in 90‐day modified Rankin scale of 0 to 2 (44% versus 47.5%, P =0.543) between patients in the RT and control groups. Conclusions In patients with ELVO with underlying ICAS requiring RT, despite longer procedure time and a more thrombectomy passes, the 90 days favorable outcomes were comparable with patients with embolic ELVO.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".