Aims. Evidence exists of a consistent decline in the value and time that medical schools place upon their undergraduate orthopaedic placements. This limited exposure to trauma and orthopaedics (T&O) during medical school will be the only experience in the speciality for the majority of doctors. This review aims to provide an overview of undergraduate orthopaedic training in the UK. Methods. This review summarizes the relevant literature from the last 20 years in the UK. Articles were selected from database searches using MEDLINE, EMBASE, ERIC, Cochrane, and Web of Science. A total of 16 papers met the inclusion criteria. Results. The length of exposure to T&O is declining; the mean total placement duration of two to three weeks is significantly less than the four- to six-week minimum advised by most relevant sources. The main teaching methods described in the literature included didactic lectures, bedside teaching, and small group case-based discussions. Students preferred interactive, blended learning teaching styles over didactic methods. This improvement in satisfaction was reflected in improvements in student assessment scores. However, studies failed to assess competencies in clinical skills and examinations, which is consistent with the opinions of UK foundation year doctors, approximately 40% of whom report a “poor” understanding of orthopaedics. Furthermore, the majority of UK doctors are not exposed to orthopaedics at the postgraduate level, which only serves to amplify the disparity between junior and generalist knowledge, and the standards expected by senior colleagues and professional bodies. Conclusion. There is a deficit in undergraduate orthopaedic training within the UK which has only worsened in the last 20 years, leaving medical students and foundation doctors with a potentially significant lack of orthopaedic knowledge. Cite this article: Bone Jt Open 2022;3(7):549–556

Aims. Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results. A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion. The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice. Cite this article: Bone Jt Open 2024;5(1):9–19

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As an increasing number of female surgeons are choosing orthopaedics, it is important to recognize the impact of pregnancy within this cohort. The aim of this review was to examine common themes and data surrounding pregnancy, parenthood, and fertility within orthopaedics.

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The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA).

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Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

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: Bone Joint J 2022;104-B(12):1292–1303.

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The aim of this study was to perform a systematic review of the evidence for the use of intraoperative cell salvage in patients undergoing revision hip arthroplasty, and specifically to analyze the available data in order to quantify any associated reduction in the use of allogenic blood transfusion, and the volume which is used.

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Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient’s functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA.

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The ‘pink, pulseless hand’ is often used to describe the clinical situation in which a child with a supracondylar fracture of the humerus has normal distal perfusion in the absence of a palpable peripheral pulse. The management guidelines are based on the assessment of perfusion, which is difficult to undertake and poorly evaluated objectively. The aim of this study was to review the available literature in order to explore the techniques available for the preoperative clinical assessment of perfusion in these patients and to evaluate the clinical implications.

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Gender bias and sexual discrimination (GBSD) have been widely recognized across a range of fields and are now part of the wider social consciousness. Such conduct can occur in the medical workplace, with detrimental effects on recipients. The aim of this review was to identify the prevalence and impact of GBSD in orthopaedic surgery, and to investigate interventions countering such behaviours.

The application of robotics in the operating theatre for knee arthroplasty remains controversial. As with all new technology, the introduction of new systems might be associated with a learning curve. However, guidelines on how to assess the introduction of robotics in the operating theatre are lacking. This systematic review aims to evaluate the current evidence on the learning curve of robot-assisted knee arthroplasty. An extensive literature search of PubMed, Medline, Embase, Web of Science, and Cochrane Library was conducted. Randomized controlled trials, comparative studies, and cohort studies were included. Outcomes assessed included: time required for surgery, stress levels of the surgical team, complications in regard to surgical experience level or time needed for surgery, size prediction of preoperative templating, and alignment according to the number of knee arthroplasties performed. A total of 11 studies met the inclusion criteria. Most were of medium to low quality. The operating time of robot-assisted total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is associated with a learning curve of between six to 20 cases and six to 36 cases respectively. Surgical team stress levels show a learning curve of seven cases in TKA and six cases for UKA. Experience with the robotic systems did not influence implant positioning, preoperative planning, and postoperative complications. Robot-assisted TKA and UKA is associated with a learning curve regarding operating time and surgical team stress levels. Future evaluation of robotics in the operating theatre should include detailed measurement of the various aspects of the total operating time, including total robotic time and time needed for preoperative planning. The prior experience of the surgical team should also be evaluated and reported.

Cite this article: Bone Joint J 2020;102-B(4):407–413.

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This study evaluates the quality of patient-reported outcome measures (PROMs) reported in childhood fracture trials and recommends outcome measures to assess and report physical function, functional capacity, and quality of life using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) standards.

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To analyze outcomes reported in studies of Ponseti correction of idiopathic clubfoot.

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To systematically review the outcomes and complications of cosmetic stature lengthening.