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
Bone is one of the most highly adaptive tissues in the body, possessing the capability to alter its morphology and function in response to stimuli in its surrounding environment. The ability of bone to sense and convert external mechanical stimuli into a biochemical response, which ultimately alters the phenotype and function of the cell, is described as mechanotransduction. This review aims to describe the fundamental physiology and biomechanisms that occur to induce osteogenic adaptation of a cell following application of a physical stimulus. Considerable developments have been made in recent years in our understanding of how cells orchestrate this complex interplay of processes, and have become the focus of research in osteogenesis. We will discuss current areas of preclinical and clinical research exploring the harnessing of mechanotransductive properties of cells and applying them therapeutically, both in the context of fracture healing and de novo bone formation in situations such as nonunion. Cite this article:
