Aims. Posterior malleolar (PM) fractures are commonly associated with ankle fractures, pilon fractures, and to a lesser extent tibial shaft fractures. The tibialis posterior (TP) tendon entrapment is a rare complication associated with PM fractures. If undiagnosed, TP entrapment is associated with complications, ranging from reduced range of ankle movement to instability and pes planus deformities, which require further surgeries including radical treatments such as arthrodesis. Methods. The inclusion criteria applied in PubMed, Scopus, and Medline database searches were: all adult studies published between 2012 and 2022; and studies written in English. Outcome of TP entrapment in patients with ankle injuries was assessed by two reviewers independently. Results. Four retrospective studies and eight case reports were accepted in this systematic review. Collectively there were 489 Pilon fractures, 77 of which presented with TP entrapment (15.75%). There were 28 trimalleolar fractures, 12 of which presented with TP entrapment (42.86%). All the case report studies reported inability to reduce the fractures at initial presentation. The diagnosis of TP entrapment was made in the early period in two (25%) cases, and delayed diagnosis in six (75%) cases reported. Using modified Clavien-Dindo complication classification, 60 (67%) of the injuries reported grade IIIa complications and 29 (33%) grade IIIb complications. Conclusion. TP tendon was the commonest tendon injury associated with pilon fracture and, to a lesser extent, trimalleolar ankle fracture. Early identification using a clinical suspicion and CT imaging could lead to early management of TP entrapment in these injuries, which could lead to better patient outcomes and reduced morbidity. Cite this article: Bone Jt Open 2024;5(3):252–259

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.