Yi-Xi Wang

Machine learning–driven prediction of in-hospital cardiac arrest among critically ill patients with traumatic spinal cord injury: a multicenter cohort study

Background In-hospital cardiac arrest (IHCA) represents a devastating complication among critically ill patients with traumatic spinal cord injury (TSCI), driven by profound autonomic dysfunction and systemic physiological instability. Despite the high mortality associated with these events, early recognition remains c...

Performance, Heterogeneity, and Methodological Quality of YOLO-Based Models for Fracture Detection: A Systematic Review and Meta-Analysis.

Immune response to DNA and RNA: structural insights, molecular mechanisms, and therapeutic targeting

Evidence is synthesized as a two-fate problem: whether an endogenous nucleic acid accumulates at these sensors to drive autoinflammation or is cleared by nucleases before detection is set by the balance between sensor engagement and clearance capacity.

A machine learning model for automated anesthesia risk classification in lumbar spinal stenosis patients: development and multicenter validation

The machine learning model developed in this study demonstrates strong capability in stratifying anesthetic risk for patients with lumbar spinal stenosis, providing valuable reference for selecting surgical and anesthetic approaches.