Aims. To evaluate the diagnostic characteristics and reliability of radiological methods used to assess scaphoid fracture union through a systematic review and meta-analysis. Methods. MEDLINE, Embase, and the Cochrane Library were searched from inception to June 2022. Any study reporting data on the diagnostic characteristics and/or the reliability of radiological methods assessing scaphoid union was included. Data were extracted and checked for accuracy and completeness by pairs of reviewers. Methodological quality was assessed using the QUADAS-2 tool. Results. A total of 13 studies were included, which were three assessed radiographs alone, six CT alone, and four radiographs + CT. Diagnostic sensitivity was assessed by CT in three studies (0.78, 0.78, and 0.73) and by radiographs in two studies (0.65, 0.75). Diagnostic specificity was assessed by CT in three studies (0.96, 0.8, 0.4) and by radiographs in two studies (0.67, 0.4). Interobserver reliability was assessed for radiographs by seven studies (two fair, four moderate, and one substantial) and for CT in nine studies (one fair, one moderate, six substantial, and one almost perfect). Conclusion. There is evidence to support both the use of CT and radiographs in assessing scaphoid fracture union. Although CT appears superior in terms of both its diagnostic characteristics and reliability, further research is necessary to better define the optimal clinical pathways for patients. Cite this article: Bone Jt Open 2025;6(3):246–253

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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.

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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.