Aims. To systematically review the predominant complication rates and changes to patient-reported outcome measures (PROMs) following osteochondral allograft (OCA) transplantation for shoulder instability. Methods. This systematic review, following PRISMA guidelines and registered in PROSPERO, involved a comprehensive literature search using PubMed, Embase, Web of Science, and Scopus. Key search terms included “allograft”, “shoulder”, “humerus”, and “glenoid”. The review encompassed 37 studies with 456 patients, focusing on primary outcomes like failure rates and secondary outcomes such as PROMs and functional test results. Results. A meta-analysis of primary outcomes across 17 studies revealed a dislocation rate of 5.1% and an increase in reoperation rates from 9.3% to 13.7% post-publication bias adjustment. There was also a noted rise in conversion to total shoulder arthroplasty and incidence of osteoarthritis/osteonecrosis over longer follow-up periods. Patient-reported outcomes and functional tests generally showed improvement, albeit with notable variability across studies. A concerning observation was the consistent presence of allograft resorption, with rates ranging from 33% to 80%. Comparative studies highlighted similar efficacy between distal tibial allografts and Latarjet procedures in most respects, with some differences in specific tests. Conclusion. OCA transplantation presents a promising treatment option for shoulder instability, effectively addressing both glenoid and humeral head defects with favourable patient-reported outcomes. These findings advocate for the inclusion of OCA transplantation in treatment protocols for shoulder instability, while also emphasizing the need for further high-quality, long-term research to better understand the procedure’s efficacy profile. Cite this article: Bone Jt Open 2024;5(7):570–580

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.