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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Due to their radiolucency and favourable mechanical properties, carbon fibre nails may be a preferable alternative to titanium nails for oncology patients. We aim to compare the surgical characteristics and short-term results of patients who underwent intramedullary fixation with either a titanium or carbon fibre nail for pathological long-bone fracture. This single tertiary-institutional, retrospectively matched case-control study included 72 patients who underwent prophylactic or therapeutic fixation for pathological fracture of the humerus, femur, or tibia with either a titanium (control group, n = 36) or carbon fibre (case group, n = 36) intramedullary nail between 2016 to 2020. Patients were excluded if intramedullary fixation was combined with any other surgical procedure/fixation method. Outcomes included operating time, blood loss, fluoroscopic time, and complications. Fisher’s exact test and Mann-Whitney U test were used for categorical and continuous outcomes, respectively.Aims
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