Prevalence of artificial intelligence (AI) algorithms within the Trauma & Orthopaedics (T&O) literature has greatly increased over the last ten years. One increasingly explored aspect of AI is the automated interpretation of free-text data often prevalent in electronic medical records (known as natural language processing (NLP)). We set out to review the current evidence for applications of NLP methodology in T&O, including assessment of study design and reporting. MEDLINE, Allied and Complementary Medicine (AMED), Excerpta Medica Database (EMBASE), and Cochrane Central Register of Controlled Trials (CENTRAL) were screened for studies pertaining to NLP in T&O from database inception to 31 December 2023. An additional grey literature search was performed. NLP quality assessment followed the criteria outlined by Farrow et al in 2021 with two independent reviewers (classification as absent, incomplete, or complete). Reporting was performed according to the Synthesis-Without Meta-Analysis (SWiM) guidelines. The review protocol was registered on the Prospective Register of Systematic Reviews (PROSPERO; registration no. CRD42022291714).Aims
Methods
Acromial fractures following reverse shoulder arthroplasty (RSA) have a wide range of incidences in reported case series. This study evaluates their incidence following RSA by systematically reviewing the current literature. A systematic review using the search terms “reverse shoulder”, “reverse total shoulder”, or “inverted shoulder” was performed using PubMed, Web of Science, and Cochrane databases between 1 January 2010 and 31 March 2018. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. Studies were included if they reported on RSA outcomes and the incidence rate of acromial and/or scapular spine fractures. The rate of these fractures was evaluated for primary RSA, revision RSA, RSA indications, and RSA implant design.Aims
Materials and Methods
