Aims. Our primary aim was to establish the proportion of female orthopaedic consultants who perform arthroplasty via cases submitted to the National Joint Registry (NJR), which covers England, Wales, Northern Ireland, the Isle of Man, and Guernsey. Secondary aims included comparing time since specialist registration, private practice participation, and number of hospitals worked in between male and female surgeons. Methods. Publicly available data from the NJR was extracted on the types of arthroplasty performed by each surgeon, and the number of procedures of each type undertaken. Each surgeon was cross-referenced with the General Medical Council (GMC) website, using GMC number to extract surgeon demographic data. These included sex, region of practice, and dates of full and specialist registration. Results. Of 2,895 surgeons contributing to the NJR in 2023, 102 (4%) were female. The highest proportions of female surgeons were among those who performed elbow (n = 25; 5%), shoulder (n = 24; 4%), and ankle (n = 8; 4%) arthroplasty. Hip (n = 66; 3%) and knee arthroplasty (n = 39; 2%) had the lowest female representation. Female surgeons had been practising for a median of 10.4 years since specialist registration compared to 13.7 years for males (p < 0.001). Northern Ireland was the region with the highest proportion of female arthroplasty surgeons (8%). A greater proportion of male surgeons worked in private practice (63% vs 24%; p < 0.001) and in multiple hospitals (74% vs 40%; p < 0.001). Conclusion. Only 4% of surgeons currently contributing cases to the NJR are female, with the highest proportion performing elbow arthroplasty (5%). Female orthopaedic surgeons in the NJR are earlier in their careers than male surgeons, and are less involved in private practice. There is a wide geographical variation in the proportion of female arthroplasty surgeons. Cite this article: Bone Jt Open 2024;5(8):637–643

Aims. 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. Methods. 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. Results. By November 2020, the elective waiting list in England is predicted to be between 885,286 and 1,028,733. If reduced hospital capacity is factored into the model, returning to full capacity by November, the waiting list could be as large as 1.4 million. With a 30% increase in productivity, it would take 20 months if there was no hidden burden of unreferred patients, and 48 months if there was a hidden burden, to return to pre-COVID-19 waiting list numbers. By August 2020, the estimated deficits of hip and knee arthroplasties from NJR data were 18,298 (44.8%) and 16,567 (38.6%), respectively, compared to the same time period in 2019. The cost to clear this black log would be £198,811,335. Conclusion. There will be up to 1.4 million patients on elective orthopaedic waiting lists in England by November 2020, approximate three-times the pre-COVID-19 average. There are various strategies for recovery to return to pre-COVID-19 waiting list numbers reliant on increasing capacity, but these have substantial cost implications. Cite this article: Bone Jt Open 2021;2(2):103–110

Aims

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.

Aims

The evidence base within trauma and orthopaedics has traditionally favoured quantitative research methodologies. Qualitative research can provide unique insights which illuminate patient experiences and perceptions of care. Qualitative methods reveal the subjective narratives of patients that are not captured by quantitative data, providing a more comprehensive understanding of patient-centred care. The aim of this study is to quantify the level of qualitative research within the orthopaedic literature.