Aims. Patients undergoing limb reconstruction surgery often face a challenging and lengthy process to complete their treatment journey. The majority of existing outcome measures do not adequately capture the patient-reported outcomes relevant to this patient group in a single measure. Following a previous systematic review, the Stanmore Limb Reconstruction Score (SLRS) was designed with the intent to address this need for an effective instrument to measure patient-reported outcomes in limb reconstruction patients. We aim to assess the face validity of this score in a pilot study. Methods. The SLRS was designed following structured interviews with several groups including patients who have undergone limb reconstruction surgery, limb reconstruction surgeons, specialist nurses, and physiotherapists. This has subsequently undergone further adjustment for language and clarity. The score was then trialled on ten patients who had undergone limb reconstruction surgery, with subsequent structured questioning to understand the perceived suitability of the score. Results. Ten patients completed the score and the subsequent structured interview. Considering the tool as a whole, 100% of respondents felt the score to be comprehensible, relevant, and comprehensive regarding the areas that were important to a patient undergoing limb reconstruction surgery. For individual questions, on a five-point Likert scale, importance/relevance was reported as a mean of 4.78 (4.3 to 5.0), with ability to understand rated as 4.92 (4.7 to 5.0) suggesting high levels of relevance and comprehension. Flesch-Kincaid reading grade level was calculated as 5.2 (10 to 11 years old). Conclusion. The current SLRS has been shown to have acceptable scores from a patient sample regarding relevance, comprehensibility, and comprehensiveness. This suggests face validity, however further testing required and is ongoing in a larger cohort of patients to determine the
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 ( Cite this article:
Orthopaedic infection is a potentially serious complication of elective and emergency trauma and orthopaedic procedures, with a high associated burden of morbidity and cost. Optimization of vitamin D levels has been postulated to be beneficial in the prevention of orthopaedic infection. This study explores the role of vitamin D in orthopaedic infection through a systematic review of available evidence. A comprehensive search was conducted on databases including Medline and Embase, as well as grey literature such as Google Scholar and The World Health Organization Database. Pooled analysis with weighted means was undertaken.Aims
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This study aims to enhance understanding of clinical and radiological consequences and involved mechanisms that led to corrosion of the Precice Stryde (Stryde) intramedullary lengthening nail in the post market surveillance era of the device. Between 2018 and 2021 more than 2,000 Stryde nails have been implanted worldwide. However, the outcome of treatment with the Stryde system is insufficiently reported. This is a retrospective single-centre study analyzing outcome of 57 consecutive lengthening procedures performed with the Stryde nail at the authors’ institution from February 2019 until November 2020. Macro- and microscopic metallographic analysis of four retrieved nails was conducted. To investigate observed corrosion at telescoping junction, scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDX) were performed.Aims
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The primary aim is to estimate the current and potential number of patients on NHS England orthopaedic elective waiting lists by November 2020. The secondary aims are to model recovery strategies; review the deficit of hip and knee arthroplasty from National Joint Registry (NJR) data; and assess the cost of returning to pre-COVID-19 waiting list numbers. A model of referral, waiting list, and eventual surgery was created and calibrated using historical data from NHS England (April 2017 to March 2020) and was used to investigate the possible consequences of unmet demand resulting from fewer patients entering the treatment pathway and recovery strategies. NJR data were used to estimate the deficit of hip and knee arthroplasty by August 2020 and NHS tariff costs were used to calculate the financial burden.Aims
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Studies have addressed the issue of increasing prevalence of work-related musculoskeletal (MSK) pain among different occupations. However, contributing factors to MSK pain have not been fully investigated among orthopaedic surgeons. Thus, this study aimed to approximate the prevalence and predictors of MSK pain among Saudi orthopaedic surgeons working in Riyadh, Saudi Arabia. A cross-sectional study using an electronic survey was conducted in Riyadh. The questionnaire was distributed through email among orthopaedic surgeons in Riyadh hospitals. Standardized Nordic questionnaires for the analysis of musculoskeletal symptoms were used. Descriptive measures for categorical and numerical variables were presented. Student’s t-test and Pearson’s χ2 test were used. The level of statistical significance was set at p ≤ 0.05.Introduction
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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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The COVID-19 pandemic presents an unprecedented burden on global healthcare systems, and existing infrastructures must adapt and evolve to meet the challenge. With health systems reliant on the health of their workforce, the importance of protection against disease transmission in healthcare workers (HCWs) is clear. This study collated responses from several countries, provided by clinicians familiar with practice in each location, to identify areas of best practice and policy so as to build consensus of those measures that might reduce the risk of transmission of COVID-19 to HCWs at work. A cross-sectional descriptive survey was designed with ten open and closed questions and sent to a representative sample. The sample was selected on a convenience basis of 27 senior surgeons, members of an international surgical society, who were all frontline workers in the COVID-19 pandemic. This study was reported according to the Standards for Reporting Qualitative Research (SRQR) checklist.Aims
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