Su, Chih-ChiChih-ChiSuLin, Yen-PoYen-PoLinYen, Hung-KuanHung-KuanYenPan, Yu-TingYu-TingPanZijlstra, HesterHesterZijlstraVerlaan, Jorrit-JanJorrit-JanVerlaanSchwab, Joseph HJoseph HSchwabLai, Cheng-YoCheng-YoLaiMING-HSIAO HUSHU-HUA YANGGroot, Olivier QOlivier QGroot2024-02-022024-02-022023-09-011067151Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/639569There are predictive algorithms for predicting 3-month and 1-year survival in patients with spinal metastasis. However, advance in surgical technique, immunotherapy, and advanced radiation therapy has enabled shortening of postoperative recovery, which returns dividends to the overall quality-adjusted life-year. As such, the Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was proposed to predict 6-week survival in patients with spinal metastasis, whereas its utility for patients treated with nonsurgical treatment was untested externally. This study aims to validate the survival prediction of the 6-week SORG-MLA for patients with spinal metastasis and provide the measurement of model consistency (MC).enjournal article10.5435/JAAOS-D-23-00091371924222-s2.0-85168252099https://scholars.lib.ntu.edu.tw/handle/123456789/636429