Objective: Postoperative delirium (POD) is a frequent complication after surgery and its occurrence is associated with poor outcomes.The pathophysiology of this complication is not clear, but identification of risk factors is important for positive postoperative outcomes.The purpose of this study was to investigate the associations between the preo
Blast-related mild traumatic brain injury (bmTBI) often leads to long-term sequalae, but diagnostic approaches are lacking due to insufficient knowledge about the predominant Dryer Fan Pulley pathophysiology.This study aimed to build a diagnostic model for future verification by applying machine-learning based support vector machine (SVM) modeling
BackgroundGastric cancer with synchronous distant metastases indicates a dismal prognosis.The success in survival improvement mainly relies on our ability to predict the potential benefit of a therapy.Our objective is to develop an artificial intelligence annotated clinical-pathologic risk model to predict its outcomes.MethodsIn participants (n=475