The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.Aims
Methods
The primary aim is to estimate the current and potential number of patients on NHS England orthopaedic elective waiting lists by November 2020. The secondary aims are to model recovery strategies; review the deficit of hip and knee arthroplasty from National Joint Registry (NJR) data; and assess the cost of returning to pre-COVID-19 waiting list numbers. A model of referral, waiting list, and eventual surgery was created and calibrated using historical data from NHS England (April 2017 to March 2020) and was used to investigate the possible consequences of unmet demand resulting from fewer patients entering the treatment pathway and recovery strategies. NJR data were used to estimate the deficit of hip and knee arthroplasty by August 2020 and NHS tariff costs were used to calculate the financial burden.Aims
Methods
The primary aim was to assess the rate of patient deferral of elective orthopaedic surgery and whether this changed with time during the coronavirus disease 2019 (COVID-19) pandemic. The secondary aim was to explore the reasons why patients wanted to defer surgery and what measures/circumstances would enable them to go forward with surgery. Patients were randomly selected from elective orthopaedic waiting lists at three centres in the UK in April, June, August, and September 2020 and were contacted by telephone. Patients were asked whether they wanted to proceed or defer surgery. Patients who wished to defer were asked seven questions relating to potential barriers to proceeding with surgery and were asked whether there were measures/circumstances that would allow them to go forward with surgery.Aims
Methods
This study aims to define the epidemiology of trauma presenting to a single centre providing all orthopaedic trauma care for a population of ∼ 900,000 over the first 40 days of the COVID-19 pandemic compared to that presenting over the same period one year earlier. The secondary aim was to compare this with population mobility data obtained from Google. A cross-sectional study of consecutive adult (> 13 years) patients with musculoskeletal trauma referred as either in-patients or out-patients over a 40-day period beginning on 5 March 2020, the date of the first reported UK COVID-19 death, was performed. This time period encompassed social distancing measures. This group was compared to a group of patients referred over the same calendar period in 2019 and to publicly available mobility data from Google.Aims
Methods
