The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.Aims
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It is well described that patients with bone and joint infections (BJIs) commonly experience significant functional impairment and disability. Published literature is lacking on the impact of BJIs on mental health. Therefore, the aim of this study was to assess health-related quality of life (HRQoL) and the impact on mental health in patients with BJIs. The AO Trauma Infection Registry is a prospective multinational registry. In total, 229 adult patients with long-bone BJI were enrolled between 1 November 2012 and 31 August 2017 in 18 centres from ten countries. Clinical outcome data, demographic data, and details on infections and treatments were collected. Patient-reported outcomes using the 36-Item Short-Form Health Survey questionnaire (SF-36), Parker Mobility Score, and Katz Index of Independence in Activities of Daily Living were assessed at one, six, and 12 months. The SF-36 mental component subscales were analyzed and correlated with infection characteristics and clinical outcome.Aims
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The timing of when to remove a circular frame is crucial; early removal results in refracture or deformity, while late removal increases the patient morbidity and delay in return to work. This study was designed to assess the effectiveness of a staged reloading protocol. We report the incidence of mechanical failure following both single-stage and two stage reloading protocols and analyze the associated risk factors. We identified consecutive patients from our departmental database. Both trauma and elective cases were included, of all ages, frame types, and pathologies who underwent circular frame treatment. Our protocol is either a single-stage or two-stage process implemented by defunctioning the frame, in order to progressively increase the weightbearing load through the bone, and promote full loading prior to frame removal. Before progression, through the process we monitor patients for any increase in pain and assess radiographs for deformity or refracture.Aims
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Patient-reported outcome measures have become an important part of routine care. The aim of this study was to determine if Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be used to create patient subgroups for individuals seeking orthopaedic care. This was a cross-sectional study of patients from Duke University Department of Orthopaedic Surgery clinics (14 ambulatory and four hospital-based). There were two separate cohorts recruited by convenience sampling (i.e. patients were included in the analysis only if they completed PROMIS measures during a new patient visit). Cohort #1 (n = 12,141; December 2017 to December 2018,) included PROMIS short forms for eight domains (Physical Function, Pain Interference, Pain Intensity, Depression, Anxiety, Sleep Quality, Participation in Social Roles, and Fatigue) and Cohort #2 (n = 4,638; January 2019 to August 2019) included PROMIS Computer Adaptive Testing instruments for four domains (Physical Function, Pain Interference, Depression, and Sleep Quality). Cluster analysis (K-means method) empirically derived subgroups and subgroup differences in clinical and sociodemographic factors were identified with one-way analysis of variance.Aims
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The ongoing COVID-19 pandemic has disrupted and delayed medical and surgical examinations where attendance is required in person. Our article aims to outline the validity of online assessment, the range of benefits to both candidate and assessor, and the challenges to its implementation. In addition, we propose pragmatic suggestions for its introduction into medical assessment. We reviewed the literature concerning the present status of online medical and surgical assessment to establish the perceived benefits, limitations, and potential problems with this method of assessment.Aims
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This study aimed to identify patients receiving total hip arthroplasty (THA) for trauma during the peak of the COVID-19 pandemic in the UK and quantify the risks of contracting SARS-CoV-2 virus, the proportion of patients requiring treatment in an intensive care unit (ICU), and rate of complications including mortality. All patients receiving a primary THA for trauma in four regional hospitals were identified for analysis during the period 1 March to 1 June 2020, which covered the current peak of the COVID-19 pandemic in the UK.Aims
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To establish if COVID-19 has worsened outcomes in patients with AO 31 A or B type hip fractures. Retrospective analysis of prospectively collected data was performed for a five-week period from 20 March 2020 and the same time period in 2019. The primary outcome was mortality at 30 days. Secondary outcomes were COVID-19 infection, perioperative pulmonary complications, time to theatre, type of anaesthesia, operation, grade of surgeon, fracture type, postoperative intensive care admission, venous thromboembolism, dislocation, infection rates, and length of stay.Aims
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The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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