Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient’s functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Eligible studies of THA patients that conducted at least one type of objective functional assessment both pre- and postoperatively were identified through Embase, Medline/PubMed, and Cochrane Central database-searching from inception to 15 September 2023. The assessments included were subgrouped for analysis: gait analysis, motion analysis, wearables, and strength tests.Aims
Methods
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
Methods
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
Gender bias and sexual discrimination (GBSD) have been widely recognized across a range of fields and are now part of the wider social consciousness. Such conduct can occur in the medical workplace, with detrimental effects on recipients. The aim of this review was to identify the prevalence and impact of GBSD in orthopaedic surgery, and to investigate interventions countering such behaviours. A systematic review was conducted by searching Medline, EMCARE, CINAHL, PsycINFO, and the Cochrane Library Database in April 2020, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to which we adhered. Original research papers pertaining to the prevalence and impact of GBSD, or mitigating strategies, within orthopaedics were included for review.Aims
Methods
Periprosthetic joint infection (PJI) is a serious complication
of total hip arthroplasty (THA). Different bearing surface materials
have different surface properties and it has been suggested that
the choice of bearing surface may influence the risk of PJI after
THA. The objective of this meta-analysis was to compare the rate
of PJI between metal-on-polyethylene (MoP), ceramic-on-polyethylene
(CoP), and ceramic-on-ceramic (CoC) bearings. Electronic databases (Medline, Embase, Cochrane library, Web
of Science, and Cumulative Index of Nursing and Allied Health Literature)
were searched for comparative randomized and observational studies
that reported the incidence of PJI for different bearing surfaces.
Two investigators independently reviewed studies for eligibility, evaluated
risk of bias, and performed data extraction. Meta-analysis was performed
using the Mantel–Haenzel method and random-effects model in accordance
with methods of the Cochrane group.Aims
Patients and Methods
