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
Methods
Aims. The value of core decompression (CD) in the treatment of
Short-stemmed femoral implants have been used for total hip arthroplasty (THA) in young and active patients to conserve bone, provide physiological loading, and reduce the incidence of thigh pain. Only short- to mid-term results have been presented and there have been concerns regarding component malalignment, incorrect sizing, and subsidence. This systematic review reports clinical and radiological outcomes, complications, revision rates, and implant survival in THA using short-stemmed femoral components. A literature review was performed using the EMBASE, Medline, and Cochrane databases. Strict inclusion and exclusion criteria were used to identify studies reporting clinical and radiological follow-up for short-stemmed hip arthroplasties.Aims
Materials and Methods