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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Scapular notching is thought to have an adverse effect on the outcome of reverse total shoulder arthroplasty (RTSA). However, the matter is still controversial. The aim of this study was to determine the clinical impact of scapular notching on outcomes after RTSA. Three electronic databases (PubMed, Cochrane Database, and EMBASE) were searched for studies which evaluated the influence of scapular notching on clinical outcome after RTSA. The quality of each study was assessed. Functional outcome scores (the Constant-Murley scores (CMS), and the American Shoulder and Elbow Surgeons (ASES) scores), and postoperative range of movement (forward flexion (FF), abduction, and external rotation (ER)) were extracted and subjected to meta-analysis. Effect sizes were expressed as weighted mean differences (WMD).Aims
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