Emergent Large Vessel Occlusion due to Intracranial Stenosis: Identification, Management, Challenges, and Future Directions
Bibliographic record
Abstract
This comprehensive literature review focuses on acute stroke related to intracranial atherosclerotic stenosis (ICAS), with an emphasis on ICAS-large vessel occlusion. ICAS is the leading cause of stroke globally, with high recurrence risk, especially in Asian, Black, and Hispanic populations. Various risk factors, including hypertension, diabetes, hyperlipidemia, smoking, and advanced age lead to ICAS, which in turn results in stroke through different mechanisms. Recurrent stroke risk in patients with ICAS with hemodynamic failure is particularly high, even with aggressive medical management. Developments in advanced imaging have improved our understanding of ICAS and ability to identify high-risk patients who could benefit from intervention. Herein, we focus on current management strategies for ICAS-large vessel occlusion discussed, including the use of perfusion imaging, endovascular therapy, and stenting. In addition, we focus on strategies that aim at identifying subjects at higher risk for early recurrent risk who could benefit from early endovascular intervention The review underscores the need for further research to optimize ICAS-large vessel occlusion treatment strategies, a traditionally understudied topic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".