Aims

To develop a multidisciplinary health research agenda (HRA) utilizing expertise from various disciplines to identify and prioritize evidence uncertainties in orthopaedics, thereby reducing research waste.

Introduction. The objective assessment of shoulder function is important for personalized diagnosis, therapies and evidence-based practice but has been limited by specialized equipment and dedicated movement laboratories. Advances in AI-driven computer vision (CV) using consumer RGB cameras (red-blue-green) and open-source CV models offer the potential for routine clinical use. However, key concepts, evidence, and research gaps have not yet been synthesized to drive clinical translation. This scoping review aims to map related literature. Method. Following the JBI Manual for Evidence Synthesis, a scoping review was conducted on PubMed and Scholar using search terms including “shoulder,” “pose estimation,” “camera”, and others. From 146 initial results, 27 papers focusing on clinical applicability and using consumer cameras were included. Analysis employed a Grounded Theory approach guided iterative refinement. Result. Studies primarily used Microsoft Kinect (infrared-based depth sensing, RGB camera; discontinued) or monocular consumer cameras with open-source CV-models, sometimes supplemented by LiDAR (laser-based depth sensing), wearables or markers. Technical validation studies against gold standards were scarce and too inconsistent for comparison. Larger range of motion (RoM) movements were accurately recorded, but smaller movements, rotations and scapula tracking remained challenging. For instance, one larger validation study comparing shoulder angles during arm raises to a marker-based gold-standard reported Pearson's R = 0.98 and a standard error of 2.4deg. OpenPose and Mediapipe were the most used CV-models. Recent efforts try to improve model performance by training with shoulder specific movements. Conclusion. Low-cost, routine clinical movement analysis to assess shoulder function using consumer cameras and CV seems feasible. It can provide acceptable accuracy for certain movement tasks and larger RoM. Capturing small, hidden or the entirety of shoulder movement requires improvements such as via training models with shoulder specific data or using dual cameras. Technical validation studies require methodological standardization, and clinical validation against established constructs is needed for translation into practice

Introduction. Lower limb amputation is associated with significant morbidity and mortality. Reflecting the predominance of vascular or diabetic disease as a cause for lower limb amputation, much of the available literature excludes lower limb amputation secondary to trauma in the reporting of complication rates. This paucity in the literature represents a research gap in describing the incidence of complications in lower limb amputation due to trauma, which we aim to address. Materials and Methods. Retrospective analysis of a prospectively collected database of all traumatic lower limb amputations secondary to trauma from a regional multidisciplinary amputee service at Queen Mary's Hospital. Electronic patient records and paper notes were consulted for evidence of re-operation, infection (superficial or deep), phantom limb pain and neuroma. 222 patients were screened and 108 included in the data analysis. Results. Records identified 108 lower limb amputations secondary to trauma in 99 patients with a mean age of 34 years (at time of amputation). Average follow-up was 225 months. 33.6% of patients underwent re-operation, 25.2% had at least one episode of infection. Of those who underwent re-operation, 47.2% had evidence of infection. 42% and 3.7% of patients described phantom limb pain and neuroma respectively. Conclusions. Lower limb amputations secondary to trauma exhibit higher rates of re-operation and infection compared to vascular or diabetic amputees. This first study to provide high quality data describing the incidence of complications such as re-operation, infection, phantom limb pain and neuroma in lower limb amputations secondary to trauma

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.

Background: Psychosocial factors are well-known contributors to the suffering and disability associated with common musculoskeletal problems. How to identify salient obstacles to recovery or return to work, and how to manage them effectively remains difficult. This project interpreted the evidence base and presented it as solution-focused guidance for everyday practical use by the key players (clinicians, employers, funders, case managers, etc) to help people remain active and working. Methods and Results: Two methods were used to identify evidence and practical advice, and synthesize this into use-able statements:. existing reviews;. an international think tank charged with producing updated reviews and identifying research gaps. An extended conceptual development of a ‘flags framework’, based on the earlier approach of Yellow Flags, was used to prepare an easily understood and pragmatic approach. The framework integrates obstacles related to the person (yellow flags), the workplace (blue flags) and the context (black flags). A full-colour 32-page document suitable for distribution as both print and electronic media was developed. This contains a clear explanation of how to identify psychosocial flags, how to develop a plan to address them effectively, and how to take action to overcome the obstacles. Poster-style summaries for clinicians, the workplace, and the individual are included, and are available for download. International consultation was used to ensure system-independent applicability and language. Conclusion: The new document provides practical guidance on identifying and managing psychosocial issues relevant to common musculoskeletal problems based on the latest evidence and conceptual approaches. Conflict of Interest: none. Sources of Funding: Society for Back Pain Research, Faculty of Occupational Medicine, BackCare, Transport for London, Royal Mail, HCML, TSO

Aims

Multiligament knee injuries (MLKI) are devastating injuries that can result in significant morbidity and time away from sport. There remains considerable variation in strategies employed for investigation, indications for operative intervention, outcome reporting, and rehabilitation following these injuries. At present no study has yet provided a comprehensive overview evaluating the extent, range, and overall summary of the published literature pertaining to MLKI. Our aim is to perform a methodologically rigorous scoping review, mapping the literature evaluating the diagnosis and management of MLKI.

Aims

Biofilm-related infection is a major complication that occurs in orthopaedic surgery. Various treatments are available but efficacy to eradicate infections varies significantly. A systematic review was performed to evaluate therapeutic interventions combating biofilm-related infections on in vivo animal models.

Aims

Orthopaedic surgeries are complex, frequently performed procedures associated with significant haemorrhage and perioperative blood transfusion. Given refinements in surgical techniques and changes to transfusion practices, we aim to describe contemporary transfusion practices in orthopaedic surgery in order to inform perioperative planning and blood banking requirements.

Trauma and orthopaedics is the largest of the surgical specialties and yet attracts a disproportionately small fraction of available national and international funding for health research. With the burden of musculoskeletal disease increasing, high-quality research is required to improve the evidence base for orthopaedic practice. Using the current research landscape in the United Kingdom as an example, but also addressing the international perspective, we highlight the issues surrounding poor levels of research funding in trauma and orthopaedics and indicate avenues for improving the impact and success of surgical musculoskeletal research.

Cite this article: Bone Joint J 2014; 96-B:1578–85.