Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Aims
Methods
Gender bias and sexual discrimination (GBSD) have been widely recognized across a range of fields and are now part of the wider social consciousness. Such conduct can occur in the medical workplace, with detrimental effects on recipients. The aim of this review was to identify the prevalence and impact of GBSD in orthopaedic surgery, and to investigate interventions countering such behaviours. A systematic review was conducted by searching Medline, EMCARE, CINAHL, PsycINFO, and the Cochrane Library Database in April 2020, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to which we adhered. Original research papers pertaining to the prevalence and impact of GBSD, or mitigating strategies, within orthopaedics were included for review.Aims
Methods