Chondrosarcoma is the second most common surgically treated primary bone sarcoma. Despite a large number of scientific papers in the literature, there is still significant controversy about diagnostics, treatment of the primary tumour, subtypes, and complications. Therefore, consensus on its day-to-day treatment decisions is needed. In January 2024, the Birmingham Orthopaedic Oncology Meeting (BOOM) attempted to gain global consensus from 300 delegates from over 50 countries. The meeting focused on these critical areas and aimed to generate consensus statements based on evidence amalgamation and expert opinion from diverse geographical regions. In parallel, periprosthetic joint infection (PJI) in oncological reconstructions poses unique challenges due to factors such as adjuvant treatments, large exposures, and the complexity of surgery. The meeting debated two-stage revisions, antibiotic prophylaxis, managing acute PJI in patients undergoing chemotherapy, and defining the best strategies for wound management and allograft reconstruction. The objectives of the meeting extended beyond resolving immediate controversies. It sought to foster global collaboration among specialists attending the meeting, and to encourage future research projects to address unsolved dilemmas. By highlighting areas of disagreement and promoting collaborative research endeavours, this initiative aims to enhance treatment standards and potentially improve outcomes for patients globally. This paper sets out some of the controversies and questions that were debated in the meeting. Cite this article: Bone Joint J 2024;106-B(5):425–429

Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article: Bone Joint J 2022;104-B(9):1011–1016

Aims. With recent progress in cancer treatment, the number of advanced-age patients with spinal metastases has been increasing. It is important to clarify the influence of advanced age on outcomes following surgery for spinal metastases, especially with a focus on subjective health state values. Methods. We prospectively analyzed 101 patients with spinal metastases who underwent palliative surgery from 2013 to 2016. These patients were divided into two groups based on age (< 70 years and ≥ 70 years). The Eastern Cooperative Oncology Group (ECOG) performance status (PS), Barthel index (BI), and EuroQol-5 dimension (EQ-5D) score were assessed at study enrolment and at one, three, and six months after surgery. The survival times and complications were also collected. Results. In total, 65 patients were aged < 70 years (mean 59.6 years; 32 to 69) and 36 patients were aged ≥ 70 years (mean 75.9 years; 70 to 90). In both groups, the PS improved from PS3 to PS1 by spine surgery, the mean BI improved from < 60 to > 80 points, and the mean EQ-5D score improved from 0.0 to > 0.7 points. However, no significant differences were found in the improvement rates and values of the PS, BI, and EQ-5D score at any time points between the two groups. The PS, BI, and EQ-5D score improved throughout the follow-up period in approximately 90% of patients in each group. However, the improved PS, BI, and EQ-5D scores subsequently deteriorated in some patients, and the redeterioration rate of the EQ-5D was significantly higher in patients aged ≥ 70 than < 70 years (p = 0.027). Conclusion. Palliative surgery for spinal metastases improved the PS, activities of daily living, and quality of life, regardless of age. However, clinicians should be aware of the higher risk of redeterioration of the quality of life in advanced-age patients. Cite this article: Bone Joint J 2020;102-B(12):1709–1716

Aims

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

Aims

The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow.