Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest

Brain CT Analysis as a Tool for Outcome Prediction after Out-of-Hospital Cardiac Arrest: A Supervised Machine Learning Analysis Research Background Out-of-Hospital Cardiac Arrest (OHCA) is one of the leading causes of death in the Western world, with extremely low survival rates, ranging from 3% to 16%. The neurological and overall outcomes after O...

Outcomes of Mechanical Thrombectomy for Acute Ischemic Stroke in Cancer Patients: A Single-Center Experience and Meta-Analysis

Research Report: Mechanical Thrombectomy Outcomes in Acute Ischemic Stroke Patients with Cancer Background Acute ischemic stroke (AIS) caused by large vessel occlusion (LVO) is a severe neurological injury, which is further complicated in cancer patients. Cancer-related stroke mechanisms include a hypercoagulable state, coagulopathies due to tumor-...

Impact of a Deep Learning Sepsis Prediction Model on Quality of Care and Survival

Impact of Deep Learning Sepsis Prediction Model on Nursing Quality and Patient Survival Research Background Sepsis is a systemic inflammatory response caused by infection, affecting approximately 48 million people globally each year, with around 11 million deaths. Due to the heterogeneity of sepsis, early identification often faces significant chal...

Outcomes of Mechanical Thrombectomy for Patients with Stroke Presenting with Low Alberta Stroke Program Early Computed Tomography Score in Early and Late Time Windows

Outcomes of Mechanical Thrombectomy for Stroke Patients with Low Alberta Stroke Program Early CT Score Background Acute ischemic stroke is a life-threatening disease, and large vessel occlusion is one of the major causes of severe disability. Mechanical thrombectomy (MT) has become the standard treatment for acute ischemic stroke, with multiple cli...