Yang, Jiun-JenJiun-JenYangCHIH-WEI CHENFourman, Mitchell SMitchell SFourmanBongers, Michiel E RMichiel E RBongersKarhade, Aditya VAditya VKarhadeGroot, Olivier QOlivier QGrootWEI-HSIN LINYen, Hung-KuanHung-KuanYenHuang, Po-HaoPo-HaoHuangSHU-HUA YANGSchwab, Joseph HJoseph HSchwabMING-HSIAO HU2021-08-102021-08-102021-02-0215299430https://scholars.lib.ntu.edu.tw/handle/123456789/577422Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and 1-year mortality of patients with metastatic cancer to the spine has been multiply validated, with a high degree of accuracy in both internal and external validation studies. However, prior external validations were conducted using patient groups located on the east coast of the United States, representing a generally homogeneous population. The aim of this study was to externally validate the SORG algorithms with a Taiwanese population.enBody mass index; Neoplasm staging; Spine metastases; Surgical oncology; Survival; Taiwanese[SDGs]SDG3albumin; alkaline phosphatase; creatinine; hemoglobin; adult; aged; Article; body mass; calibration; cancer mortality; cancer patient; cancer staging; cancer survival; clinical assessment; cohort analysis; comparative study; female; histology; human; humajournal article10.1016/j.spinee.2021.01.027335453712-s2.0-85101105258https://scholars.lib.ntu.edu.tw/handle/123456789/552623