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Introduction:. Immersive virtual reality (iVR) allows surgical trainees to practice skills without risking harm to patients or the need for cadaveric training resources. However, iVR has never been directly compared with cadaver training, the longtime gold standard for surgical skill training. We aimed to compare skill acquisition using cadaver laboratory and iVR training methods for augmented baseplate implantation during reverse total shoulder arthroplasty (rTSA). Methods:. In a randomized controlled trial, junior orthopaedic surgery residents were assigned to a 1-hour training with either iVR or a cadaveric laboratory session with shoulder specimens. Before training, all participants viewed an overview lecture and technique video demonstrating key steps of augmented baseplate implantation for rTSA. Participants were assessed by a blinded evaluator using validated competency checklists during cadaveric glenoid baseplate implantation. Continuous and categorial variables were analyzed using the 2-sample t test and Fisher exact test. Results:. Fourteen junior residents (3 incoming matched postgraduate year [PGY1], 6 PGY1s, 1 PGY2, and 4 PGY3s) were randomized to training with either iVR (n = 6) or cadaver laboratory (n = 8). There were no significant differences in demographic data, previous experience with rTSA, or previous use of iVR (p > 0.05). There were no significant difference in total Objective Structured Assessment of Technical Skill score (91.2% [15.2] vs. 93.25% [6.32], −0.1406 to 0.1823, p = 0.763), Global Rating Scale score (4.708 [0.459] vs. 4.609 [0.465], −0.647 to 0.450, p = 0.699), or time to completion (546 seconds [158] vs. 591 seconds [192], −176.3 to 266.8, p = 0.655) in cadaveric glenoid baseplate implantation. Average cost of iVR hardware and a 1-year software license was $4,900, and average cost of a single cadaver laboratory was $1,268.20 per resident. Conclusions:. Among junior orthopaedic residents, there is similar skill acquisition when training with either cadaver laboratory or iVR. Although additional research into this field is needed, iVR may provide an important and cost-effective tool in surgical education. Clinical Relevance:. Emerging simulation and iVR technology simulation in surgical training programs can increase access to effective and high-level surgical training across the globe and improve quality of care.

Background: Machine learning has shown potential in accurately predicting outcomes after orthopedic surgery, thereby allowing for improved patient selection, risk stratification, and preoperative planning. This study sought to develop machine learning models to predict nonhome discharge after total shoulder arthroplasty (TSA). Methods: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective TSA from 2012 to 2018. Boosted decision tree and artificial neural networks (ANN) machine learning models were developed to predict non-home discharge and 30-day postoperative complications. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and overall accuracy (%). Multivariate binary logistic regression analyses were used to identify variables that were significantly associated with the predicted outcomes. Results: There were 21,544 elective TSA cases identified in the National Surgical Quality Improvement Program registry from 2012 to 2018 that met inclusion criteria. Multivariate logistic regression identified several variables associated with increased risk of nonhome discharge including female sex (odds ratio [OR] = 2.83; 95% confidence interval [CI] = 2.53-3.17; P