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
The use of patient-reported outcome measures (PROMs) to assess the outcome after total knee (TKA) and total hip arthroplasty (THA) is increasing, with associated regulatory mandates. However, the robustness and clinical relevance of long-term data are often questionable. It is important to determine whether using long-term PROMs data justify the resources, costs, and difficulties associated with their collection. The aim of this study was to assess studies involving TKA and THA to determine which PROMs are most commonly reported, how complete PROMs data are at ≥ five years postoperatively, and the extent to which the scores change between early and long-term follow-up. We conducted a systematic review of the literature. Randomized controlled trials (RCTs) with sufficient reporting of PROMs were included. The mean difference in scores from the preoperative condition to early follow-up times (between one and two years), and from early to final follow-up, were calculated. The mean rates of change in the scores were calculated from representative studies. Meta-analyses were also performed on the most frequently reported PROMs.Aims
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