Aims. 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. Methods. 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). Results. The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson’s correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient’s CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. Conclusion. Oxford Hip Score, Oxford Knee Score, Oxford Shoulder Score, and Oxford Elbow Score (including separate subscale scores) CATs all markedly reduce the burden of items to be completed without sacrificing score accuracy. Cite this article: Bone Jt Open 2022;3(10):786–794

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (. https://www.ideal-collaboration.net/. ). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams. Cite this article: Bone Joint Res 2024;13(9):507–512

Aims. The worldwide COVID-19 pandemic is directly impacting the field of orthopaedic surgery and traumatology with postponed operations, changed status of planned elective surgeries and acute emergencies in patients with unknown infection status. To this point, Germany's COVID-19 infection numbers and death rate have been lower than those of many other nations. Methods. This article summarizes the current regimen used in the field of orthopaedics in Germany during the COVID-19 pandemic. Internal university clinic guidelines, latest research results, expert consensus, and clinical experiences were combined in this article guideline. Results. Every patient, with and without symptoms, should be screened for COVID-19 before hospital admission. Patients should be assigned to three groups (infection status unknown, confirmed, or negative). Patients with unknown infection status should be considered as infectious. Dependent of the infection status and acuity of the symptoms, patients are assigned to a COVID-19-free or affected zone of the hospital. Isolation, hand hygiene, and personal protective equipment is essential. Hospital personnel directly involved in the care of COVID-19 patients should be tested on a weekly basis independently of the presence of clinical symptoms, staff in the COVID-19-free zone on a biweekly basis. Class 1a operation rooms with laminar air flow and negative pressure are preferred for surgery in COVID-19 patients. Electrocautery should only be utilized with a smoke suction system. In cases of unavoidable elective surgery, a self-imposed quarantine of 14 days is recommended prior to hospital admission. Conclusion. During the current COVID-19 pandemic, orthopaedic patients admitted to the hospital should be treated based on an interdisciplinary algorithm, strictly separating infectious and non-infectious cases. Cite this article: Bone Joint Open 2020;1-6:309–315

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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.

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Systematic reviews of randomized controlled trials (RCTs) are the highest level of evidence used to inform patient care. However, it has been suggested that the quality of randomization in RCTs in orthopaedic surgery may be low. This study aims to describe the quality of randomization in trials included in systematic reviews in orthopaedic surgery.

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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 smartphone, can assess structural human bone properties in under three seconds.

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This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms.

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Excision of chronic osteomyelitic bone creates a dead space which must be managed to avoid early recurrence of infection. Systemic antibiotics cannot penetrate this space in high concentrations, so local treatment has become an attractive adjunct to surgery. The aim of this study was to present the mid- to long-term results of local treatment with gentamicin in a bioabsorbable ceramic carrier.

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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.

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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.

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COVID-19 has changed the practice of orthopaedics across the globe. The medical workforce has dealt with this outbreak with varying strategies and adaptations, which are relevant to its field and to the region. As one of the ‘hotspots’ in the UK , the surgical branch of trauma and orthopaedics need strategies to adapt to the ever-changing landscape of COVID-19.

Virtual encounters have experienced an exponential rise amid the current COVID-19 crisis. This abrupt change, seen in response to unprecedented medical and environmental challenges, has been forced upon the orthopaedic community. However, such changes to adopting virtual care and technology were already in the evolution forecast, albeit in an unpredictable timetable impeded by regulatory and financial barriers. This adoption is not meant to replace, but rather augment established, traditional models of care while ensuring patient/provider safety, especially during the pandemic. While our department, like those of other institutions, has performed virtual care for several years, it represented a small fraction of daily care. The pandemic required an accelerated and comprehensive approach to the new reality. Contemporary literature has already shown equivalent safety and patient satisfaction, as well as superior efficiency and reduced expenses with musculoskeletal virtual care (MSKVC) versus traditional models. Nevertheless, current literature detailing operational models of MSKVC is scarce. The current review describes our pre-pandemic MSKVC model and the shift to a MSKVC pandemic workflow that enumerates the conceptual workflow organization (patient triage, from timely care provision based on symptom acuity/severity to a continuum that includes future follow-up). Furthermore, specific setup requirements (both resource/personnel requirements such as hardware, software, and network connectivity requirements, and patient/provider characteristics respectively), and professional expectations are outlined. MSKVC has already become a pivotal element of musculoskeletal care, due to COVID-19, and these changes are confidently here to stay. Readiness to adapt and evolve will be required of individual musculoskeletal clinical teams as well as organizations, as established paradigms evolve.

Cite this article: Bone Joint Open 2020;1-6:272–280.

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Hip fracture patients are at higher risk of severe COVID-19 illness, and admission into hospital puts them at further risk. We implemented a two-site orthopaedic trauma service, with ‘COVID’ and ‘COVID-free’ hubs, to deliver urgent and infection-controlled trauma care for hip fracture patients, while increasing bed capacity for medical patients during the COVID-19 pandemic.