The standard of wide tumour-like resection for chronic osteomyelitis (COM) has been challenged recently by adequate debridement. This paper reviews the evolution of surgical debridement for long bone COM, and presents the outcome of adequate debridement in a tertiary bone infection unit. We analyzed the retrospective record review from 2014 to 2020 of patients with long bone COM. All were managed by multidisciplinary infection team (MDT) protocol. Adequate debridement was employed for all cases, and no case of wide resection was included.Aims
Methods
In response to the COVID-19 pandemic, there was a rapidly implemented restructuring of UK healthcare services. The The Royal National Orthopaedic Hospital, Stanmore, became a central hub for the provision of trauma services for North Central/East London (NCEL) while providing a musculoskeletal tumour service for the south of England, the Midlands, and Wales and an urgent spinal service for London. This study reviews our paediatric practice over this period in order to share our experience and lessons learned. Our hospital admission pathways are described and the safety of surgical and interventional radiological procedures performed under general anaesthesia (GA) with regards to COVID-19 in a paediatric population are evaluated. All paediatric patients (≤ 16 years) treated in our institution during the six-week peak period of the pandemic were included. Prospective data for all paediatric trauma and urgent elective admissions and retrospective data for all sarcoma admissions were collected. Telephone interviews were conducted with all patients and families to assess COVID-19 related morbidity at 14 days post-discharge.Introduction
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
