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. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
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Older adults with hip fractures are at high risk of experiencing complications after surgery, but estimates of the rate of specific complications vary by study design and follow-up period. The aim of this systematic review was to determine the prevalence of complications in older adults after hip fracture surgery. MEDLINE, Embase, CINAHL, and CENTRAL databases were searched from inception until 30 June 2023. Studies were included if they reported prevalence data of complications in an unselected, consecutive population of older adults (aged ≥ 60 years) undergoing hip fracture surgery.Aims
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The aims of this study were to identify and evaluate the current literature examining the prognostic factors which are associated with failure of total elbow arthroplasty (TEA). Electronic literature searches were conducted using MEDLINE, Embase, PubMed, and Cochrane. All studies reporting prognostic estimates for factors associated with the revision of a primary TEA were included. The risk of bias was assessed using the Quality In Prognosis Studies (QUIPS) tool, and the quality of evidence was assessed using the modified Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. Due to low quality of the evidence and the heterogeneous nature of the studies, a narrative synthesis was used.Aims
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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. 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.Aims
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Robotic-assisted unicompartmental knee arthroplasty (R-UKA) has been proposed as an approach to improve the results of the conventional manual UKA (C-UKA). The aim of this meta-analysis was to analyze the studies comparing R-UKA and C-UKA in terms of clinical outcomes, radiological results, operating time, complications, and revisions. The literature search was conducted on three databases (PubMed, Cochrane, and Web of Science) on 20 February 2024 according to the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Inclusion criteria were comparative studies, written in the English language, with no time limitations, on the comparison of R-UKA and C-UKA. The quality of each article was assessed using the Downs and Black Checklist for Measuring Quality.Aims
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This systematic review aims to compare the precision of component positioning, patient-reported outcome measures (PROMs), complications, survivorship, cost-effectiveness, and learning curves of MAKO robotic arm-assisted unicompartmental knee arthroplasty (RAUKA) with manual medial unicompartmental knee arthroplasty (mUKA). Searches of PubMed, MEDLINE, and Google Scholar were performed in November 2021 according to the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “unicompartmental”, “knee”, and “arthroplasty”. Published clinical research articles reporting the learning curves and cost-effectiveness of MAKO RAUKA, and those comparing the component precision, functional outcomes, survivorship, or complications with mUKA, were included for analysis.Aims
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Metal allergy in knee arthroplasty patients is a controversial topic. We aimed to conduct a scoping review to clarify the management of metal allergy in primary and revision total knee arthroplasty (TKA). Studies were identified by searching electronic databases: Cochrane Central Register of Controlled Trials, Ovid MEDLINE, and Embase, from their inception to November 2020, for studies evaluating TKA patients with metal hypersensitivity/allergy. All studies reporting on diagnosing or managing metal hypersensitivity in TKA were included. Data were extracted and summarized based on study design, study population, interventions and outcomes. A practical guide is then formulated based on the available evidence.Aims
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