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
The extended wait that most patients are now experiencing for hip and knee arthroplasty has raised questions about whether reliance on waiting time as the primary driver for prioritization is ethical, and if other additional factors should be included in determining surgical priority. Our Prioritization of THose aWaiting hip and knee ArthroplastY (PATHWAY) project will explore which perioperative factors are important to consider when prioritizing those on the waiting list for hip and knee arthroplasty, and how these factors should be weighted. The final product will include a weighted benefit score that can be used to aid in surgical prioritization for those awaiting elective primary hip and knee arthroplasty. There will be two linked work packages focusing on opinion from key stakeholders (patients and surgeons). First, an online modified Delphi process to determine a consensus set of factors that should be involved in patient prioritization. This will be performed using standard Delphi methodology consisting of multiple rounds where following initial individual rating there is feedback, discussion, and further recommendations undertaken towards eventual consensus. The second stage will then consist of a Discrete Choice Experiment (DCE) to allow for priority setting of the factors derived from the Delphi through elicitation of weighted benefit scores. The DCE consists of several choice tasks designed to elicit stakeholder preference regarding included attributes (factors).Aims
Methods
We studied the effects of the timing of tourniquet release in 88 patients randomly allocated for release after wound closure and bandaging (group A), or before the quadriceps layer had been closed allowing control of bleeding before suture (group B). The groups were similar in mean age, weight, gender, preoperative knee score, radiographic grading, and prosthesis implanted. Patients in group B had less postoperative pain, achieved earlier straight-leg raising, and had fewer wound complications. Five patients in group A had to return to theatre, three for manipulation under anaesthesia, one for secondary closure of wound dehiscence, and one for drainage of a haematoma. The last patient later developed a deep infection, which was treated by a two-stage revision. There were no significant differences between the two groups in operating time, or the decrease in haemoglobin concentration at 48 hours postoperatively. Some of the adverse effects of the use of a tourniquet for knee surgery can be significantly reduced by early tourniquet release, with haemostasis before the quadriceps mechanism and the wound are closed.
