Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
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Dislocation is the most common indication for further surgery following total hip arthroplasty (THA) when undertaken in patients with a femoral neck fracture. This study aimed to assess the complication rates of THA with dual mobility components (THA-DMC) following a femoral neck fracture and to compare outcomes between THA-DMC, conventional THA, and hemiarthroplasty (HA). We performed a systematic review of all English language articles on THA-DMC published between 2010 and 2019 in the MEDLINE, EMBASE, and Cochrane databases. After the application of rigorous inclusion and exclusion criteria, 23 studies dealing with patients who underwent treatment for a femoral neck fracture using THA-DMC were analyzed for the rate of dislocation. Secondary outcomes included reoperation, periprosthetic fracture, infection, mortality, and functional outcome. The review included 7,189 patients with a mean age of 77.8 years (66.4 to 87.6) and a mean follow-up of 30.9 months (9.0 to 68.0).Aims
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