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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Open lower limb fracture is a life-changing injury affecting 11.5 per 100,000 adults each year, and causes significant morbidity and resource demand on trauma infrastructures. This study aims to identify what, and how, outcomes have been reported for people following open lower limb fracture over ten years. Systematic literature searches identified all clinical studies reporting outcomes for adults following open lower limb fracture between January 2009 and July 2019. All outcomes and outcome measurement instruments were extracted verbatim. An iterative process was used to group outcome terms under standardized outcome headings categorized using an outcome taxonomy.Aims
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