Kunze KN, Polce EM, Rasio J, et al. - This study was sought to construct machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy. Researchers queried consecutive primary hip arthroscopy patients between January 2012-January 2017. They derived minimal clinically significant variation (MCID) for the visual analog scale (VAS) for satisfaction applying an anchor-based method and used it as the primary outcome. They enrolled a total of 935 individuals, of which 148 (15.8%) failed to achieve the MCID for VAS Satisfaction at a minimum of two-years postoperatively. After hip arthroscopy, supervised machine learning algorithms conferred excellent discrimination and performance for predicting clinically significant satisfaction, though this analysis was conducted on a single population of patients. External validation is needed to validate the performance of these algorithms.