Aims. The standard of wide tumour-like
Aims. The aticularis genu (AG) is the least substantial and deepest muscle of the anterior compartment of the thigh and of uncertain significance. The aim of the study was to describe the anatomy of AG in cadaveric specimens, to characterize the relevance of AG in pathological distal femur specimens, and to correlate the anatomy and pathology with preoperative magnetic resonance imaging (MRI) of AG. Methods. In 24 cadaveric specimens, AG was identified, photographed, measured, and dissected including neurovascular supply. In all, 35 resected distal femur specimens were examined. AG was photographed and measured and its utility as a surgical margin examined. Preoperative MRIs of these cases were retrospectively analyzed and assessed and its utility assessed as an anterior soft tissue margin in surgery. In all cadaveric specimens, AG was identified as a substantial structure, deep and separate to vastus itermedius (VI) and separated by a clear fascial plane with a discrete neurovascular supply. Mean length of AG was 16.1 cm ( ± 1.6 cm) origin anterior aspect distal third femur and insertion into suprapatellar bursa. In 32 of 35 pathological specimens, AG was identified (mean length 12.8 cm ( ± 0.6 cm)). Where AG was used as anterior cover in pathological specimens all surgical margins were clear of disease. Of these cases, preoperative MRI identified AG in 34 of 35 cases (mean length 8.8 cm ( ± 0.4 cm)). Results. AG was best visualized with T1-weighted axial images providing sufficient cover in 25 cases confirmed by pathological findings.These results demonstrate AG as a discrete and substantial muscle of the anterior compartment of the thigh, deep to VI and useful in providing anterior soft tissue margin in distal femoral
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 aim of this study is to report the 30 day COVID-19 related morbidity and mortality of patients assessed as SARS-CoV-2 negative who underwent emergency or urgent orthopaedic surgery in the NHS during the peak of the COVID-19 pandemic. A retrospective, single centre, observational cohort study of all patients undergoing surgery between 17 March 2020 and 3May 2020 was performed. Outcomes were stratified by British Orthopaedic Association COVID-19 Patient Risk Assessment Tool. Patients who were SARS-CoV-2 positive at the time of surgery were excluded.Introduction
Method
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