Kumar V, Roche C, Overman S, et al. - This study was intended to compare the accuracy correlated with a full feature set predictive model (e.g. full model =291 parameters) and a minimal feature set model (e.g. abbreviated model =19 input parameters) to prognosticate clinical outcomes in order to evaluate the efficacy of applying a minimal feature set of inputs as a shoulder arthroplasty clinical decision-support tool. Researchers examined a clinical data from 2,153 primary anatomic Total Shoulder Arthroplasty (aTSA) patients and 3,621 primaryreverse Total Shoulder Arthroplasty (rTSA) patients applying the XGBoost machine learning technique to create and test predictive models for multiple outcome measures at different postoperative time points applying a full and abbreviated model. The findings revealed that the full and abbreviated machine learning models achieved similar accuracy to predict clinical outcomes after aTSA and rTSA at multiple postoperative time points. These outcomes illustrate the efficient utilization of machine learning algorithms to prognosticate clinical outcomes. The data suggested that the use of a minimal feature set of only 19 preoperative inputs implies that this tool may be easily applied during a surgical consultation to improve decision-making related to shoulder arthroplasty.