Sniderman J, Stark RB, Schwartz CE, et al. - A machine-learning model was performed to distinguish patient-specific variables that prognosticate the postoperative functional outcome in total hip arthroplasty (THA). Researchers conducted a prospective longitudinal cohort including a total of 160 consecutive patients who had undergone total hip replacement for the treatment of degenerative arthritis completed self-reported measures pre-operatively and at 3 months post-operatively. Patient-reported health, cognitive appraisal processes, and surgical approach), a machine-learning model utilizing Least Absolute Shrinkage Selection Operator was constructed to predict post-operative Hip Disability and Osteoarthritis Outcome Score at 3-months using four types of independent variables (patient demographics. It was shown that in THA, this clinical prediction model revealed that the factors most predictive of outcome were cognitive appraisal processes, demonstrating their importance to outcome-based research.