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

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.

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The aims of this study were to identify and evaluate the current literature examining the prognostic factors which are associated with failure of total elbow arthroplasty (TEA).

Aims

Preprint servers allow authors to publish full-text manuscripts or interim findings prior to undergoing peer review. Several preprint servers have extended their services to biological sciences, clinical research, and medicine. The purpose of this study was to systematically identify and analyze all articles related to Trauma & Orthopaedic (T&O) surgery published in five medical preprint servers, and to investigate the factors that influence the subsequent rate of publication in a peer-reviewed journal.

Patient-reported outcome measures (PROMs) are being used increasingly in total knee arthroplasty (TKA). We conducted a systematic review aimed at identifying psychometrically sound PROMs by appraising their measurement properties. Studies concerning the development and/or evaluation of the measurement properties of PROMs used in a TKA population were systematically retrieved via PubMed, Web of Science, Embase, and Scopus. Ratings for methodological quality and measurement properties were conducted according to updated COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology. Of the 155 articles on 34 instruments included, nine PROMs met the minimum requirements for psychometric validation and can be recommended to use as measures of TKA outcome: Oxford Knee Score (OKS); OKS–Activity and Participation Questionnaire (OKS-APQ); 12-item short form Knee Injury and Osteoarthritis Outcome (KOOS-12); KOOS Physical function Short form (KOOS-PS); Western Ontario and McMaster Universities Arthritis Index-Total Knee Replacement function short form (WOMAC-TKR); Lower Extremity Functional Scale (LEFS); Forgotten Joint Score (FJS); Patient’s Knee Implant Performance (PKIP); and University of California Los Angeles (UCLA) activity score. The pain and function subscales in WOMAC, as well as the pain, function, and quality of life subscales in KOOS, were validated psychometrically as standalone subscales instead of as whole instruments. However, none of the included PROMs have been validated for all measurement properties. Thus, further studies are still warranted to evaluate those PROMs. Use of the other 25 scales and subscales should be tempered until further studies validate their measurement properties.

Cite this article: Bone Joint Res 2021;10(3):203–217.

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

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This review aims to summarize the outcomes used to describe effectiveness of treatments for paediatric wrist fractures within existing literature.