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
Preprint servers allow authors to publish full-text manuscripts or interim findings prior to undergoing peer review. Several preprint servers have extended their services to biological sciences, clinical research, and medicine. The purpose of this study was to systematically identify and analyze all articles related to Trauma & Orthopaedic (T&O) surgery published in five medical preprint servers, and to investigate the factors that influence the subsequent rate of publication in a peer-reviewed journal. All preprints covering T&O surgery were systematically searched in five medical preprint servers (medRxiv, OSF Preprints, Preprints.org, PeerJ, and Research Square) and subsequently identified after a minimum of 12 months by searching for the title, keywords, and corresponding author in Google Scholar, PubMed, Scopus, Embase, Cochrane, and the Web of Science. Subsequent publication of a work was defined as publication in a peer-reviewed indexed journal. The rate of publication and time to peer-reviewed publication were assessed. Differences in definitive publication rates of preprints according to geographical origin and level of evidence were analyzed.Aims
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