Managing class imbalance in the training of a large language model to predict patient selection for total knee arthroplasty: Results from the Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project

Use of class-weighting appears to provide the optimal method of training a an LLM to perform analytical tasks on free-text clinical information in the face of significant data bias ('class imbalance'). Such knowledge is an important consideration in the development of high-performance clinical AI models within Trauma and Orthopaedics.