Aims. The aim of this systematic review and meta-analysis was to gather epidemiological information on selected musculoskeletal injuries and to provide pooled injury-specific incidence rates. Methods. PubMed (National Library of Medicine) and Scopus (Elsevier) databases were searched. Articles were eligible for inclusion if they reported incidence rate (or count with population at risk), contained data on adult population, and were written in English language. The number of cases and population at risk were collected, and the pooled incidence rates (per 100,000 person-years) with 95% confidence intervals (CIs) were calculated by using either a fixed or random effects model. Results. The screening of titles yielded 206 articles eligible for inclusion in the study. Of these, 173 (84%) articles provided sufficient information to be included in the pooled incidence rates. Incidences of fractures were investigated in 154 studies, and the most common fractures in the whole adult population based on the pooled incidence rates were distal radius fractures (212.0, 95% CI 178.1 to 252.4 per 100,000 person-years), finger fractures (117.1, 95% CI 105.3 to 130.2 per 100,000 person-years), and hip fractures (112.9, 95% CI 82.2 to 154.9 per 100,000 person-years). The most common sprains and dislocations were ankle sprains (429.4, 95% CI 243.0 to 759.0 per 100,000 person-years) and first-time patellar dislocations (32.8, 95% CI 21.6 to 49.7 per 100,000 person-years). The most common injuries were anterior cruciate ligament (17.5, 95% CI 6.0 to 50.2 per 100,000 person-years) and Achilles (13.7, 95% CI 9.6 to 19.5 per 100,000 person-years) ruptures. Conclusion. The presented pooled incidence estimates serve as important references in assessing the global economic and social burden of musculoskeletal injuries. Cite this article: Bone Joint Res 2022;11(11):814–825

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.

Background

Due to the overwhelming demand for trauma services, resulting from increasing emergency department attendances over the past decade, virtual fracture clinics (VFCs) have become the fashion to keep up with the demand and help comply with the BOA Standards for Trauma and Orthopaedics (BOAST) guidelines. In this article, we perform a systematic review asking, “How useful are VFCs?”, and what injuries and conditions can be treated safely and effectively, to help decrease patient face to face consultations. Our primary outcomes were patient satisfaction, clinical efficiency and cost analysis, and clinical outcomes.