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) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis. Methods. We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes. Results. Following the final screening process, 25 articles satisfied the eligibility criteria for our review. Of these, 60% focused on tabular data while the remaining 40% analyzed image data. Among them, 16 studies were dedicated to developing new models and nine used transfer learning to leverage existing pretrained models. Additionally, three of these models underwent external validation to confirm their reliability and effectiveness. Conclusion. ML algorithms used in TSA demonstrated fair to good performance, as evidenced by the reported metrics. Integrating these models into daily clinical practice could revolutionize TSA, enhancing both surgical precision and patient outcome predictions. Despite their potential, the lack of transparency and generalizability in many current models poses a significant challenge, limiting their clinical utility. Future research should prioritize addressing these limitations to truly propel the field forward and maximize the benefits of ML in enhancing patient care. Cite this article: Bone Jt Open 2025;6(2):126–134

Aims

It is important to understand the rate of complications associated with the increasing burden of revision shoulder arthroplasty. Currently, this has not been well quantified. This review aims to address that deficiency with a focus on complication and reoperation rates, shoulder outcome scores, and comparison of anatomical and reverse prostheses when used in revision surgery.

Aims

Return to sport following undergoing total (TKA) and unicompartmental knee arthroplasty (UKA) has been researched with meta-analyses and systematic reviews of varying quality. The aim of this study is to create an umbrella review to consolidate the data into consensus guidelines for returning to sports following TKA and UKA.