REPEATED ENDOVASCULAR THROMBECTOMY FOR EARLY RECURRENT INTRACRANIAL LARGE VESSEL OCCLUSION
Bibliographic record
Abstract
Background and Purpose:Early recurrent intracranial large vessel occlusion (LVO) is uncommon and associated with poor outcome. The aim of this study was to describe the clinical and radiological features of patients with early recurrent LVO managed with repeat endovascular treatment.Methods:A retrospective analysis of ischemic stroke patients with LVO and more than one endovascular procedure within 10 days of the index stroke, treated at 9 stroke centers in New Zealand, Australia, Taiwan, Finland and Canada. Results:There were 35 patients [16 (46%) female; median age 67 (interquartile range, IQR: 50-76) years; 25 (71%) recurrent anterior circulation, 8 (23%) recurrent posterior circulation, 2 (6%) both anterior and posterior circulation LVO] included. Twenty (57%) had cardioembolic stroke etiology. The median time between procedures was 45 (IQR 16-120) hours and one patient had three procedures. Twenty-two patients (63%) had reocclusion in the same target vessel, 9 (41%) of whom had residual vessel wall irregularity after the initial procedure. Good outcomes (mRS 0-2) were achieved in 10 (29%) patients and favourable outcomes (mRS 0-3) in 17(49%). Patients with favourable outcome had lower baseline NIHSS (12 vs 18,p=0.05) and less recurrent posterior circulation LVOs (6% vs 39%,p=0.04). Two patients (6%) had symptomatic intracerebral hemorrhage. Eleven patients (31%) had died by 3 months. Conclusion:Early recurrent LVO can be safely managed with repeat endovascular treatment with favorable clinical outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".