Articles: surgery.
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This study sought to quantify radiographic differences in psoas morphology, great vessel anatomy, and lumbar lordosis between supine and prone intraoperative positioning to optimize surgical planning and minimize the risk of neurovascular injury. ⋯ Relative to the vertebral body, the psoas demonstrated substantial lateral mobility when prone, and posterior retraction specifically at L5. IVC and right iliac vein experienced significant anterior mobility-particularly at more cephalad levels. Prone position enhanced segmental lordosis and may be critical to optimizing sagittal restoration.
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This study presents the clinical characteristics, imaging manifestations, and surgical experience in 38 patients diagnosed with craniofacial fibrous dysplasia in fronto-orbital region (foFD). ⋯ In the surgical treatment of foFD, it is crucial to achieve maximal bone resection and repair skull defects, while decompressing the optic canal can provide significant benefits for patients with decreased visual function preoperatively. The use of preformed artificial materials offers advantages in aesthetic restoration after lesion excision.
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Journal of neurotrauma · Jan 2024
Prognosis at your fingertips: a machine learning-based web application for outcome prediction in acute traumatic epidural hematoma.
Traumatic brain injury (TBI) affects 69 million people worldwide each year, and acute traumatic epidural hematoma (atEDH) is a frequent and severe consequence of TBI. The aim of the study is to use machine learning (ML) algorithms to predict in-hospital death, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients with atEDH and incorporate the resulting ML models into a user-friendly web application for use in the clinical settings. The American College of Surgeons (ACS) Trauma Quality Program (TQP) database was used to identify patients with atEDH. ⋯ This study aimed to improve the prognostication of patients with atEDH using ML algorithms and developed a web application for easy integration in clinical practice. It found that ML algorithms can aid in risk stratification and have significant potential for predicting in-hospital outcomes. Results demonstrated excellent performance for predicting in-hospital death and fair performance for non-home discharges, prolonged LOS and ICU-LOS, and poor performance for major complications.