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
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Performance indicators are increasingly used to evaluate the quality of healthcare provided to patients with a hip fracture. The aim of this review was to map the variety of performance indicators used around the world and how they are defined. We present a mixed methods systematic review of literature on the use of performance indicators in hip fracture care. Evidence was searched through 12 electronic databases and other sources. A Mixed Methods Appraisal Tool was used to assess methodological quality of studies meeting the inclusion criteria. A protocol for a suite of related systematic reviews was registered at PROSPERO (CRD42023417515).Aims
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