Objectives. This review aims to summarize the outcomes used to describe effectiveness of treatments for paediatric wrist fractures within existing literature. Method. We searched the Cochrane Library, Scopus, and Ovid Medline for studies pertaining to paediatric wrist fractures. Three authors independently identified and reviewed eligible studies. This resulted in a list of
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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To analyze outcomes reported in studies of Ponseti correction of idiopathic clubfoot. A systematic review of the literature was performed to identify a list of outcomes and outcome tools reported in the literature. A total of 865 studies were screened following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and 124 trials were included in the analysis. Data extraction was completed by two researchers for each trial. Each outcome tool was assigned to one of the five core areas defined by the Outcome Measures Recommended for use in Randomized Clinical Trials (OMERACT). Bias assessment was not deemed necessary for the purpose of this paper.Aims
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