Symptomatic spinal stenosis is a very common problem, and decompression surgery has been shown to be superior to nonoperative treatment in selected patient groups. However, performing an instrumented fusion in addition to decompression may avoid revision and improve outcomes. The aim of the SpInOuT feasibility study was to establish whether a definitive randomized controlled trial (RCT) that accounted for the spectrum of pathology contributing to spinal stenosis, including pelvic incidence-lumbar lordosis (PI-LL) mismatch and mobile spondylolisthesis, could be conducted. As part of the SpInOuT-F study, a pilot randomized trial was carried out across five NHS hospitals. Patients were randomized to either spinal decompression alone or spinal decompression plus instrumented fusion. Patient-reported outcome measures were collected at baseline and three months. The intended sample size was 60 patients.Aims
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Despite limited clinical scientific backing, an additional trochanteric stabilizing plate (TSP) has been advocated when treating unstable trochanteric fractures with a sliding hip screw (SHS). We aimed to explore whether the TSP would result in less post operative fracture motion, compared to SHS alone. Overall, 31 patients with AO/OTA 31-A2 trochanteric fractures were randomized to either a SHS alone or a SHS with an additional TSP. To compare postoperative fracture motion, radiostereometric analysis (RSA) was performed before and after weightbearing, and then at four, eight, 12, 26, and 52 weeks. With the “after weightbearing” images as baseline, we calculated translations and rotations, including shortening and medialization of the femoral shaft.Aims
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Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method. We retrospectively collected CT imaging data of 108 hips with non-traumatic osteonecrosis of the femoral head from 76 consecutive patients (mean age 34.3 years (SD 8.1), 56.58% male (n = 43)) in two clinical centres. The femoral head was divided into 288 standard units (based on the orientation of units within the femoral head, designated as N[Superior], S[Inferior], E[Anterior], and W[Posterior]) using a new measurement system called the longitude and latitude division system (LLDS). A computer-aided design (CAD) measurement tool was also developed to visualize the measurement of the spatial location of necrotic lesions in CT images. Two orthopaedic surgeons independently performed measurements, and the results were used to draw 2D and 3D heat maps of spatial distribution of necrotic lesions in the femoral head, and for statistical analysis.Aims
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To investigate the extent of bone development around the scaffold of custom triflange acetabular components (CTACs) over time. We performed a single-centre historical prospective cohort study, including all patients with revision THA using the aMace CTAC between January 2017 and March 2021. A total of 18 patients (18 CTACs) were included. Models of the hemipelvis and the scaffold component of the CTACs were created by segmentation of CT scans. The CT scans were performed immediately postoperatively and at least one year after surgery. The amount of bone in contact with the scaffold was analyzed at both times, and the difference was calculated.Aims
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Accurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables. This study included 31 patients who underwent temporary hemiepiphysiodesis for varus or valgus deformity of the leg between 2014 and 2020. The skeletal age at surgical intervention was evaluated using the CPH method with long leg radiographs. The true final adult height (FHTRUE) was determined when the growth plates were closed. The final height prediction accuracy of four different multiplier tables (1. Bayley and Pinneau; 2. Paley et al; 3. Sanders – Greulich and Pyle (SGP); and 4. Sanders – peak height velocity (PHV)) was then compared using either skeletal age or chronological age.Aims
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The primary aim was to estimate the cost-effectiveness of routine operative fixation for all patients with humeral shaft fractures. The secondary aim was to estimate the health economic implications of using a Radiographic Union Score for HUmeral fractures (RUSHU) of < 8 to facilitate selective fixation for patients at risk of nonunion. From 2008 to 2017, 215 patients (mean age 57 yrs (17 to 18), 61% female (n = 130/215)) with a nonoperatively managed humeral diaphyseal fracture were retrospectively identified. Union was achieved in 77% (n = 165/215) after initial nonoperative management, with 23% (n = 50/215) uniting after surgery for nonunion. The EuroQol five-dimension three-level health index (EQ-5D-3L) was obtained via postal survey. Multiple regression was used to determine the independent influence of patient, injury, and management factors upon the EQ-5D-3L. An incremental cost-effectiveness ratio (ICER) of < £20,000 per quality-adjusted life-year (QALY) gained was considered cost-effective.Aims
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Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
The aim of this study was to assess the ability of morphological spinal parameters to predict the outcome of bracing in patients with adolescent idiopathic scoliosis (AIS) and to establish a novel supine correction index (SCI) for guiding bracing treatment. Patients with AIS to be treated by bracing were prospectively recruited between December 2016 and 2018, and were followed until brace removal. In all, 207 patients with a mean age at recruitment of 12.8 years (SD 1.2) were enrolled. Cobb angles, supine flexibility, and the rate of in-brace correction were measured and used to predict curve progression at the end of follow-up. The SCI was defined as the ratio between correction rate and flexibility. Receiver operating characteristic (ROC) curve analysis was carried out to assess the optimal thresholds for flexibility, correction rate, and SCI in predicting a higher risk of progression, defined by a change in Cobb angle of ≥ 5° or the need for surgery.Aims
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The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS).Aims
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