{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T03:29:38Z","timestamp":1761017378858,"version":"build-2065373602"},"reference-count":32,"publisher":"BMJ","issue":"9","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"unspecified","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2022,4,28]]},"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>Shoulder instability is a common injury, with a reported incidence of 23.9 per 100\u2009000 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 (&lt;2%\u2009and up to 30%, respectively). Methods to determine risk of recurrence have been developed; however, patient-specific decision-making tools are still lacking. Artificial intelligence and machine learning algorithms use self-learning complex models that can be used to make patient-specific decision-making tools. The aim of the current study is to develop and train a machine learning algorithm to create a prediction model to be used in clinical practice\u2014as an online prediction tool\u2014to estimate recurrence rates following a Bankart repair.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and analysis<\/jats:title>\n                  <jats:p>This is a multicentre retrospective cohort study. Patients with traumatic anterior shoulder dislocations that were treated with an arthroscopic Bankart repair without remplissage will be included. This study includes two parts. Part 1, collecting all potential factors influencing the recurrence rate following an arthroscopic Bankart repair in patients using multicentre data, aiming to include data from &gt;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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Ethics and dissemination<\/jats:title>\n                  <jats:p>For safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation \u2018Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies\u2019. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/bmjopen-2021-055346","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T11:35:24Z","timestamp":1662636924000},"page":"e055346","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":6,"title":["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"],"prefix":"10.1136","volume":"12","author":[{"given":"Sanne H","family":"van Spanning","sequence":"first","affiliation":[{"name":"Orthopaedic Surgery, OLVG, Amsterdam, Noord-Holland, The Netherlands"},{"name":"Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France"},{"name":"Department of Human Movement Sciences, Faculty of 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