There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
The Canadian Orthopaedic Trauma Society was started in an endeavour to answer the difficult problem of obtaining enough patients to perform top-quality research into fractures. By maintaining a high standard, including randomised study design, inclusivity, open discussion among surgeons and excellent long-term follow-up, this group has become a leader in the orthopaedic research community. This annotation describes the short history, important components and spirit necessary to build a research community or team which will function well despite the difficult research environment facing individual surgeons.
