NCBI PubMed Central
open access
Deep-learning-derived neuroimaging biomarkers of sarcopenia as predictors of outcome in endovascular thrombectomy in large vessel occlusion acute ischemic stroke.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Strokectomy in Malignant Cerebral Media Infarction#1363738
- Biomarkers to Predict Hemorrhage and Outcomes After Endovascular Treatment in Patients With Acute Large Vessel Occlusion#878267
- Magnetic resonance imaging quantitative T2* mapping to predict the red blood cell content of in vivo thrombi retrieved from patients with large vessel occlusions in acute ischemic stroke#268505
- [Plasma metabolomics combined with machine learning for classifying ischemic stroke subtypes and predicting the etiology of cryptogenic stroke].#1158754
- Blood Biomarkers to Predict the Prognosis of a Stroke Patient Undergoing Mechanical Thrombectomy#1293805
Record · ID 1374069
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive