Aims. The COVID-19 pandemic drastically affected elective orthopaedic services globally as routine orthopaedic activity was largely halted to combat this global threat. Our institution (University College London Hospital, UK) previously showed that during the first peak, a large proportion of patients were hesitant to be listed for their elective lower limb procedure. The aim of this study is to assess if there is a patient perception change towards having elective surgery now that we have passed the peak of the second wave of the pandemic. Methods. This is a prospective study of 100 patients who were on the waiting list of a
Aims. As the peak of the COVID-19 pandemic passes, the challenge shifts to safe resumption of routine medical services, including elective orthopaedic surgery. Protocols including pre-operative self-isolation, COVID-19 testing, and surgery at a non-COVID-19 site have been developed to minimize risk of transmission. Despite this, it is likely that many patients will want to delay surgery for fear of contracting COVID-19. The aim of this study is to identify the number of patients who still want to proceed with planned elective orthopaedic surgery in this current environment. Methods. This is a prospective,
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
Methods