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https://www.selleckchem.com/products/cc-90011.html Swimmers, football, and basketball players are less likely to present with PSSbut are still more likely than other types of athletes to develop the condition. Clinician awareness of PSS in athletes is critical to avoid delays in treatment and misdiagnosis, and to allow for a timely return to sport with minimal complications. In athletes presenting with upper extremity pain and swelling with a history of playing baseball or weight lifting, PSS should be higher on a clinicians differential diagnosis list. Swimmers, football, and basketball players are less likely to present with PSS but are still more likely than other types of athletes to develop the condition. Clinician awareness of PSS in athletes is critical to avoid delays in treatment and misdiagnosis, and to allow for a timely return to sport with minimal complications. Machine learning (ML) techniques have been shown to successfully predict postoperative complications for high-volume orthopedic procedures such as hip and knee arthroplasty and to stratify patients for risk-adjusted bundled payments. The latter has not been done for more heterogeneous, lower-volume procedures such as total shoulder arthroplasty (TSA) with equally limited discussion around strategies to optimize the predictive ability of ML algorithms. The purpose of this study was to (1) assess which of 5 ML algorithms best predicts 30-day readmission, (2) test select ML strategies to optimize the algorithms, and (3) report on which patient variables contribute most to risk prediction in TSA across algorithms. We identified 9043 patients in the American College of Surgeons National Surgical Quality Improvement Databasewho underwent primary TSA between 2011 and 2015. Predictors included demographics, comorbidities, laboratory data, and intraoperative variables. The outcome of interest was 30-day unplanned of 0.18. In addition, SVM was most sensitive to loss of single features, whereas the perform

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