The COVID-19 pandemic has disrupted the provision of arthroplasty services in England, Wales, and Northern Ireland. This study aimed to quantify the backlog, analyze national trends, and predict time to recovery. We performed an analysis of the mandatory prospective national registry of all independent and publicly funded hip, knee, shoulder, elbow, and ankle replacements in England, Wales, and Northern Ireland between January 2019 and December 2022 inclusive, totalling 729,642 operations. The deficit was calculated per year compared to a continuation of 2019 volume. Total deficit of cases between 2020 to 2022 was expressed as a percentage of 2019 volume. Sub-analyses were performed based on procedure type, country, and unit sector.Aims
Methods
To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.Aims
Methods
