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This study aims to define a set of family-centred core outcomes for infants undergoing brace treatment to facilitate consistent reporting for future high-quality research.

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Prolonged waits for hip and knee arthroplasty have raised questions about the equity of current approaches to waiting list prioritization for those awaiting surgery. We therefore set out to understand key stakeholder (patient and surgeon) preferences for the prioritization of patients awaiting such surgery, in order to guide future waiting list redesign.

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Surgeon and patient reluctance to participate are potential significant barriers to conducting placebo-controlled trials of orthopaedic surgery. Understanding the preferences of orthopaedic surgeons and patients regarding the design of randomized placebo-controlled trials (RCT-Ps) of knee procedures can help to identify what RCT-P features will lead to the greatest participation. This information could inform future trial designs and feasibility assessments.

Periprosthetic joint infection (PJI) represents a complex challenge in orthopaedic surgery associated with substantial morbidity and healthcare expenditures. The debridement, antibiotics, and implant retention (DAIR) protocol is a viable treatment, offering several advantages over exchange arthroplasty. With the evolution of treatment strategies, considerable efforts have been directed towards enhancing the efficacy of DAIR, including the development of a phased debridement protocol for acute PJI management. This article provides an in-depth analysis of DAIR, presenting the outcomes of single-stage, two-stage, and repeated DAIR procedures. It delves into the challenges faced, including patient heterogeneity, pathogen identification, variability in surgical techniques, and antibiotics selection. Moreover, critical factors that influence the decision-making process between single- and two-stage DAIR protocols are addressed, including team composition, timing of the intervention, antibiotic regimens, and both anatomical and implant-related considerations. By providing a comprehensive overview of DAIR protocols and their clinical implications, this annotation aims to elucidate the advancements, challenges, and potential future directions in the application of DAIR for PJI management. It is intended to equip clinicians with the insights required to effectively navigate the complexities of implementing DAIR strategies, thereby facilitating informed decision-making for optimizing patient outcomes.

Cite this article: Bone Joint J 2024;106-B(12):1377–1384.

The December 2024 Children’s orthopaedics Roundup³⁶⁰ looks at: Establishing best practice for managing idiopathic toe walking in children: a UK consensus; Long-term outcomes of below-elbow casting in paediatric diaphyseal forearm fractures; Residual dysplasia risk persists in developmental dysplasia of the hip patients after Pavlik harness treatment; 3D printing in paediatricorthopaedics: enhancing surgical efficiency and patient outcomes; Pavlik harness treatment for hip dysplasia does not delay motor skill development in children; High prevalence of hip dysplasia found in adolescents with idiopathic scoliosis on routine spine radiographs; Minifragment plates as effective growth modulation for ulnar deformities of the distal radius in children; Long-term success of Chiari pelvic osteotomy in preserving hip function: 30-year follow-up study.

As advancements in total knee arthroplasty progress at an exciting pace, two areas are of special interest, as they directly impact implant design and surgical decision making. Knee morphometry considers the three-dimensional shape of the articulating surfaces within the knee joint, and knee phenotyping provides the ability to categorize alignment into practical groupings that can be used in both clinical and research settings. This annotation discusses the details of these concepts, and the ways in which they are helping us better understand the individual subtleties of each patient’s knee.

Cite this article: Bone Joint J 2024;106-B(12):1363–1368.

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

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The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

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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.

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This study aimed to investigate the optimal sagittal positioning of the uncemented femoral component in total knee arthroplasty to minimize the risk of aseptic loosening and periprosthetic fracture.

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The aim of this study was to gain a consensus for best practice of the assessment and management of children with idiopathic toe walking (ITW) in order to provide a benchmark for practitioners and guide the best consistent care.

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Endoprosthetic reconstruction following distal femur tumour resection has been widely advocated. In this paper, we present the design of an uncemented endoprosthesis system featuring a short, curved stem, with the goal of enhancing long-term survivorship and functional outcomes.

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Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement.

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The aim of this study was to determine the clinical outcomes and factors contributing to failure of transposition osteotomy of the acetabulum (TOA), a type of spherical periacetabular osteotomy, for advanced osteoarthritis secondary to hip dysplasia.

Aims. Shoulder arthroplasty is effective in the management of end-stage glenohumeral joint arthritis. However, it is major surgery and patients must balance multiple factors when considering the procedure. An understanding of patients’ decision-making processes may facilitate greater support of those considering shoulder arthroplasty and inform the outcomes of future research. Methods. Participants were recruited from waiting lists of three consultant upper limb surgeons across two NHS hospitals. Semi-structured interviews were conducted with 12 participants who were awaiting elective shoulder arthroplasty. Transcribed interviews were analyzed using a grounded theory approach. Systematic coding was performed; initial codes were categorized and further developed into summary narratives through a process of discussion and refinement. Data collection and analyses continued until thematic saturation was reached. Results. Two overall categories emerged: the motivations to consider surgery, and the information participants used to inform their decision-making. Motivations were, broadly, the relief of pain and the opportunity to get on with life and regain independence. When participants’ symptoms and restrictions prevented them enjoying life to a sufficient extent, this provided the motivation to proceed with surgery. Younger participants tended to focus on maintaining employment and recreational activities, and older patients were eager to make the most of their remaining lifetime. Participants gathered information from a range of sources and were keen to optimize their recovery where possible. An important factor for participants was whether they trusted their surgeon and were prepared to delegate responsibility for elements of their care. Conclusion. Relief of pain and the opportunity to get on with life were the primary reasons to undergo shoulder arthroplasty. Participants highlighted the importance of the patient-surgeon relationship and the need for accurate information in an accessible format which is relevant to people of different ages and functional demands. Cite this article: Bone Jt Open 2024;5(7):543–549

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This study aims to identify the top unanswered research priorities in the field of knee surgery using consensus-based methodology.

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Proximal femur fractures treatment can involve anterograde nailing with a single or double cephalic screw. An undesirable failure for this fixation is screw cut-out. In a single-screw nail, a tip-apex distance (TAD) greater than 25 mm has been associated with an increased risk of cut-out. The aim of the study was to examine the role of TAD as a risk factor in a cephalic double-screw nail.

INTRODUCTION. Simulation plays an important role in surgical education and the ability to perfect surgical performance. Simulation can be enhanced by adding various layers of realism to the experience. Haptic feedback enhances the simulation experience by providing tactile responses and virtual reality imagery provides an immersive experience and allows for greater appreciation of three-dimensional structures. In this study, we present a proof-of-concept haptic simulator to replicate key steps of a cervical laminoplasty procedure. The technology uses affordable components and is easily modifiable so that it can be used from novice through to expert level. Custom models can be easily added ensuring the simulator can be used in a wide range of orthopaedic applications from baseline education through to day of surgery pre-operative simulation. METHOD. We used the Unity Game Engine, the 3D Systems “Touch” Haptic Feedback Device (HFD), and a Meta Quest VR headset. Our system uses a number of complex algorithms to track the shape and provide haptic feedback of a virtual bone model. This allows for simulation of various tools including a high-speed burr, Kerrison rongeur and intraoperative X-rays. RESULTS. Our simulator replicates the tactile sensations of bone-burring tasks. Although we focused on the cervical laminoplasty procedure, the system can load data from CT scans, enabling the simulation of multiple other procedures. The parts cost of our system, $10,000 NZD, is a fraction of the cost of traditional surgical simulators. DISCUSSION. Our simulator reduces financial barriers to accessing orthopaedic simulators. Trainees can perform hands-on practice without compromising patient safety. The immersive nature of VR, combined with realistic haptic feedback, enables trainees to develop the dexterity and three-dimensional understanding of detailed bony work. Further refinements are needed before we can perform validation studies on our system. CONCLUSIONS. We present an affordable surgical simulator capable of simulating bony surgical procedures in a VR environment using haptic feedback technology and consumer-grade components. ACKNOWLEDGEMENTS. This research was made possible by the generosity of the Wishbone Trust

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The aim of this study was to determine the fracture haematoma (fxH) proteome after multiple trauma using label-free proteomics, comparing two different fracture treatment strategies.

OpenPredictor, a machine learning-enabled clinical decision aid, has been developed to manage backlogs in elective surgeries. It aims to optimise the use of high volume, low complexity surgical pathways by accurately stratifying patient risk, thereby facilitating the allocation of patients to the most suitable surgical sites. The tool augments elective surgical pathways by providing automated secondary opinions for perioperative risk assessments, enhancing decision-making. Its primary application is in elective sites utilising lighter pre-assessment methods, identifying patients with minimal complication risks and those high-risk individuals who may benefit from early pre-assessment. The Phase 1 clinical evaluation of OpenPredictor entailed a prospective analysis of 156 patient records from elective hip and knee joint replacement surgeries. Using a polynomial logistic regression model, patients were categorised into high, moderate, and low-risk groups. This categorisation incorporated data from various sources, including patient demographics, co-morbidities, blood tests, and overall health status. In identifying patients at risk of postoperative complications, OpenPredictor demonstrated parity with consultant-led preoperative assessments. It accurately flagged 70% of patients who later experienced complications as moderate or high risk. The tool's efficiency in risk prediction was evidenced by its balanced accuracy (75.6%), sensitivity (70% with a 95% confidence interval of 62.05% to 76.91%), and a high negative predictive value (96.7%). OpenPredictor presents a scalable and consistent solution for managing elective surgery pathways, comparable in performance to secondary consultant opinions. Its integration into pre-assessment workflows assists in efficient patient categorisation, reduces late surgery cancellations, and optimises resource allocation. The Phase 1 evaluation of OpenPredictor underscores its potential for broader clinical application and highlights the need for ongoing data refinement and system integration to enhance its performance