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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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.

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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.