Development and training of a machine learning algorithm to identify patients at risk for recurrence following an arthroscopic Bankart repair (CLEARER): protocol for a retrospective, multicentre, cohort study

IntroductionShoulder instability is a common injury, with a reported incidence of 23.9 per 100 000 person-years. There is still an ongoing debate on the most effective treatment strategy. Non-operative treatment has recurrence rates of up to 60%, whereas operative treatments such as the Bankart repair and bone block procedures show lower recurrence rates (16% and 2%, respectively) but higher complication rates (1000 patients worldwide. Part 2, the multicentre data will be re-evaluated (and where applicable complemented) using machine learning algorithms to predict outcomes. Recurrence will be the primary outcome measure.Ethics and disseminationFor safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation ‘Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies’. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.