To determine whether obesity and malnutrition have a synergistic effect on outcomes from skeletal trauma or elective orthopaedic surgery. Electronic databases including MEDLINE, Global Health, Embase, Web of Science, ScienceDirect, and PEDRo were searched up to 14 April 2024, as well as conference proceedings and the reference lists of included studies. Studies were appraised using tools according to study design, including the Oxford Levels of Evidence, the Institute of Health Economics case series quality appraisal checklist, and the CLARITY checklist for cohort studies. Studies were eligible if they reported the effects of combined malnutrition and obesity on outcomes from skeletal trauma or elective orthopaedic surgery.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:
Due to the overwhelming demand for trauma services, resulting from increasing emergency department attendances over the past decade, virtual fracture clinics (VFCs) have become the fashion to keep up with the demand and help comply with the BOA Standards for Trauma and Orthopaedics (BOAST) guidelines. In this article, we perform a systematic review asking, “How useful are VFCs?”, and what injuries and conditions can be treated safely and effectively, to help decrease patient face to face consultations. Our primary outcomes were patient satisfaction, clinical efficiency and cost analysis, and clinical outcomes. We performed a systematic literature search of all papers pertaining to VFCs, using the search engines PubMed, MEDLINE, and the Cochrane Database, according to the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) checklist. Searches were carried out and screened by two authors, with final study eligibility confirmed by the senior author.Background
Methods
The aim of this study was to assess the quality and scope of the current cost-effectiveness analysis (CEA) literature in the field of hand and upper limb orthopaedic surgery. We conducted a systematic review of MEDLINE and the CEA Registry to identify CEAs that were conducted on or after 1 January 1997, that studied a procedure pertaining to the field of hand and upper extremity surgery, that were clinical studies, and that reported outcomes in terms of quality-adjusted life-years. We identified a total of 33 studies that met our inclusion criteria. The quality of these studies was assessed using the Quality of Health Economic Analysis (QHES) scale.Aims
Materials and Methods
