The primary aim of this study was to assess the feasibility of recruiting and retaining patients to a patient-blinded randomized controlled trial comparing corticosteroid injection (CSI) to autologous protein solution (APS) injection for the treatment of subacromial shoulder pain in a community care setting. The study focused on recruitment rates and retention of participants throughout, and collected data on the interventions’ safety and efficacy. Participants were recruited from two community musculoskeletal treatment centres in the UK. Patients were eligible if aged 18 years or older, and had a clinical diagnosis of subacromial impingement syndrome which the treating clinician thought was suitable for treatment with a subacromial injection. Consenting patients were randomly allocated 1:1 to a patient-blinded subacromial injection of CSI (standard care) or APS. The primary outcome measures of this study relate to rates of recruitment, retention, and compliance with intervention and follow-up to determine feasibility. Secondary outcome measures relate to the safety and efficacy of the interventions.Aims
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Paediatric fractures are highly prevalent and are most often treated with plaster. The application and removal of plaster is often an anxiety-inducing experience for children. Decreasing the anxiety level may improve the patients’ satisfaction and the quality of healthcare. Virtual reality (VR) has proven to effectively distract children and reduce their anxiety in other clinical settings, and it seems to have a similar effect during plaster treatment. This study aims to further investigate the effect of VR on the anxiety level of children with fractures who undergo plaster removal or replacement in the plaster room. A randomized controlled trial was conducted. A total of 255 patients were included, aged five to 17 years, who needed plaster treatment for a fracture of the upper or lower limb. Randomization was stratified for age (five to 11 and 12 to 17 years). The intervention group was distracted with VR goggles and headphones during the plaster treatment, whereas the control group received standard care. As the primary outcome, the post-procedural level of anxiety was measured with the Child Fear Scale (CFS). Secondary outcomes included the children’s anxiety reduction (difference between CFS after and CFS before plaster procedure), numerical rating scale (NRS) pain, NRS satisfaction of the children and accompanying parents/guardians, and the children’s heart rates during the procedure. An independent-samples Aims
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The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
The aim of this study was to identify the information topics that should be addressed according to the parents of children with developmental dysplasia of the hip (DDH) in the diagnostic and treatment phase during the first year of life. Second, we explored parental recommendations to further optimize the information provision in DDH care. A qualitative study with semi-structured interviews was conducted between September and December 2020. A purposive sample of parents of children aged younger than one year, who were treated for DDH with a Pavlik harness, were interviewed until data saturation was achieved. A total of 20 interviews with 22 parents were conducted. Interviews were audio recorded, transcribed verbatim, independently reviewed, and coded into categories and themes.Aims
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In the UK, the agricultural, military, and construction sectors have stringent rules about the use of hearing protection due to the risk of noise-induced hearing loss. Orthopaedic staff may also be at risk due to the use of power tools. The UK Health and Safety Executive (HSE) have clear standards as to what are deemed acceptable occupational levels of noise on A-weighted and C-weighted scales. The aims of this review were to assess the current evidence on the testing of exposure to noise in orthopaedic operating theatres to see if it exceeds these regulations. A search of PubMed and EMBASE databases was conducted using PRISMA guidelines. The review was registered prospectively in PROSPERO. Studies which assessed the exposure to noise for orthopaedic staff in operating theatres were included. Data about the exposure to noise were extracted from these studies and compared with the A-weighted and C-weighted acceptable levels described in the HSE regulations.Aims
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Reimers migration percentage (MP) is a key measure to inform decision-making around the management of hip displacement in cerebral palsy (CP). The aim of this study is to assess validity and inter- and intra-rater reliability of a novel method of measuring MP using a smart phone app (HipScreen (HS) app). A total of 20 pelvis radiographs (40 hips) were used to measure MP by using the HS app. Measurements were performed by five different members of the multidisciplinary team, with varying levels of expertise in MP measurement. The same measurements were repeated two weeks later. A senior orthopaedic surgeon measured the MP on picture archiving and communication system (PACS) as the gold standard and repeated the measurements using HS app. Pearson’s correlation coefficient (r) was used to compare PACS measurements and all HS app measurements and assess validity. Intraclass correlation coefficient (ICC) was used to assess intra- and inter-rater reliability.Aims
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Aims. Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a
Periprosthetic hip-joint infection is a multifaceted and highly detrimental outcome for patients and clinicians. The incidence of prosthetic joint infection reported within two years of primary hip arthroplasty ranges from 0.8% to 2.1%. Costs of treatment are over five-times greater in people with periprosthetic hip joint infection than in those with no infection. Currently, there are no national evidence-based guidelines for treatment and management of this condition to guide clinical practice or to inform clinical study design. The aim of this study is to develop guidelines based on evidence from the six-year INFection and ORthopaedic Management (INFORM) research programme. We used a consensus process consisting of an evidence review to generate items for the guidelines and online consensus questionnaire and virtual face-to-face consensus meeting to draft the guidelines.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:
The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents’ full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson’s correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments’ minimal clinically important difference (MCID).Aims
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We aim to explore the potential technologies for monitoring and assessment of patients undergoing arthroplasty by examining selected literature focusing on the technology currently available and reflecting on possible future development and application. The reviewed literature indicates a large variety of different hardware and software, widely available and used in a limited manner, to assess patients’ performance. There are extensive opportunities to enhance and integrate the systems which are already in existence to develop patient-specific pathways for rehabilitation. Cite this article:
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:
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:
To determine the value of scoliosis surgery, it is necessary to evaluate outcomes in domains that matter to patients. Since randomized trials on adolescent idiopathic scoliosis (AIS) are scarce, prospective cohort studies with comparable outcome measures are important. To enhance comparison, a core set of patient-related outcome measures is available. The aim of this study was to evaluate the outcomes of AIS fusion surgery at two-year follow-up using the core outcomes set. AIS patients were systematically enrolled in an institutional registry. In all, 144 AIS patients aged ≤ 25 years undergoing primary surgery (median age 15 years (interquartile range 14 to 17) were included. Patient-reported (condition-specific and health-related quality of life (QoL); functional status; back and leg pain intensity) and clinician-reported outcomes (complications, revision surgery) were recorded. Changes in patient-reported outcome measures (PROMs) were analyzed using Friedman’s analysis of variance. Clinical relevancy was determined using minimally important changes (Scoliosis Research Society (SRS)-22r), cut-off values for relevant effect on functioning (pain scores) and a patient-acceptable symptom state (PASS; Oswestry Disability Index).Aims
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Deep surgical site infection (SSI) is common after lower limb fracture. We compared the diagnosis of deep SSI using alternative methods of data collection and examined the agreement of clinical photography and in-person clinical assessment by the Centers for Disease Control and Prevention (CDC) criteria after lower limb fracture surgery. Data from two large, UK-based multicentre randomized controlled major trauma trials investigating SSI and wound healing after surgical repair of open lower limb fractures that could not be primarily closed (UK WOLLF), and surgical incisions for fractures that were primarily closed (UK WHiST), were examined. Trial interventions were standard wound care management and negative pressure wound therapy after initial surgical debridement. Wound outcomes were collected from 30 days to six weeks. We compared the level of agreement between wound photography and clinical assessment of CDC-defined SSI. We are also assessed the level of agreement between blinded independent assessors of the photographs.Aims
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This study assesses patient barriers to successful telemedicine care in orthopaedic practices in a large academic practice in the COVID-19 era. In all, 381 patients scheduled for telemedicine visits with three orthopaedic surgeons in a large academic practice from 1 April 2020 to 12 June 2020 were asked to participate in a telephone survey using a standardized Institutional Review Board-approved script. An unsuccessful telemedicine visit was defined as patient-reported difficulty of use or reported dissatisfaction with teleconferencing. Patient barriers were defined as explicitly reported barriers of unsatisfactory visit using a process-based satisfaction metric. Statistical analyses were conducted using analysis of variances (ANOVAs), ranked ANOVAs, post-hoc pairwise testing, and chi-squared independent analysis with 95% confidence interval.Aims
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