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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Avulsion of the proximal hamstring tendon origin can result in significant functional impairment, with surgical re-attachment of the tendons becoming an increasingly recognized treatment. The aim of this study was to assess the outcomes of surgical management of proximal hamstring tendon avulsions, and to compare the results between acute and chronic repairs, as well as between partial and complete injuries. PubMed, CINAHL, SPORTdiscuss, Cochrane Library, EMBASE, and Web of Science were searched. Studies were screened and quality assessed.Aims
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