Aims. Orthopaedic surgeries are complex, frequently performed procedures associated with significant haemorrhage and perioperative blood transfusion. Given refinements in
Safety concerns surrounding osseointegration are a significant barrier to replacing socket prosthesis as the standard of care following limb amputation. While implanted osseointegrated prostheses traditionally occur in two stages, a one-stage approach has emerged. Currently, there is no existing comparison of the outcomes of these different approaches. To address safety concerns, this study sought to determine whether a one-stage osseointegration procedure is associated with fewer adverse events than the two-staged approach. A comprehensive electronic search and quantitative data analysis from eligible studies were performed. Inclusion criteria were adults with a limb amputation managed with a one- or two-stage osseointegration procedure with follow-up reporting of complications.Aims
Methods
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
The current global pandemic due to COVID-19 is generating significant burden on the health service in the UK. On 23 March 2020, the UK government issued requirements for a national lockdown. The aim of this multicentre study is to gain a greater understanding of the impact lockdown has had on the rates, mechanisms and types of injuries together with their management across a regional trauma service. Data was collected from an adult major trauma centre, paediatric major trauma centre, district general hospital, and a regional hand trauma unit. Data collection included patient demographics, injury mechanism, injury type and treatment required. Time periods studied corresponded with the two weeks leading up to lockdown in the UK, two weeks during lockdown, and the same two-week period in 2019.Aims
Methods
There is widespread variation in the management of rare orthopaedic disease, in a large part owing to uncertainty. No individual surgeon or hospital is typically equipped to amass sufficient numbers of cases to draw robust conclusions from the information available to them. The programme of research will establish the British Orthopaedic Surgery Surveillance (BOSS) Study; a nationwide reporting structure for rare disease in orthopaedic surgery. The BOSS Study is a series of nationwide observational cohort studies of pre-specified orthopaedic disease. All relevant hospitals treating the disease are invited to contribute anonymised case details. Data will be collected digitally through REDCap, with an additional bespoke software solution used to regularly confirm case ascertainment, prompt follow-up reminders and identify potential missing cases from external sources of information (i.e. national administrative data). With their consent, patients will be invited to enrich the data collected by supplementing anonymised case data with patient reported outcomes. The study will primarily seek to calculate the incidence of the rare diseases under investigation, with 95% confidence intervals. Descriptive statistics will be used to describe the case mix, treatment variations and outcomes. Inferential statistical analysis may be used to analyze associations between presentation factors and outcomes. Types of analyses will be contingent on the disease under investigation.Introduction
Methods