The use of patient-reported outcome measures (PROMs) to assess the outcome after total knee (TKA) and total hip arthroplasty (THA) is increasing, with associated regulatory mandates. However, the robustness and clinical relevance of long-term data are often questionable. It is important to determine whether using long-term PROMs data justify the resources, costs, and difficulties associated with their collection. The aim of this study was to assess studies involving TKA and THA to determine which PROMs are most commonly reported, how complete PROMs data are at ≥ five years postoperatively, and the extent to which the scores change between early and long-term follow-up. We conducted a systematic review of the literature. Randomized controlled trials (RCTs) with sufficient reporting of PROMs were included. The mean difference in scores from the preoperative condition to early follow-up times (between one and two years), and from early to final follow-up, were calculated. The mean rates of change in the scores were calculated from representative studies. Meta-analyses were also performed on the most frequently reported PROMs.Aims
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Frozen shoulder is a common and debilitating condition characterized by pain and restricted movement at the glenohumeral joint. Various treatment methods have been explored to alleviate symptoms, with suprascapular nerve block (SSNB) emerging as a promising intervention. This meta-analysis aimed to assess the effectiveness of SSNB in treating frozen shoulder. The study protocol was registered with PROSPERO (CRD42023475851). We searched the MEDLINE, Embase, and Cochrane Library databases in November 2023. Randomized controlled trials (RCTs) comparing SSNB against other interventions were included. The primary outcome was any functional patient-reported outcome measure. Secondary outcomes were the visual analogue scale (VAS) for pain, range of motion (ROM), and complications. Risk of bias was assessed using the Cochrane risk of bias v. 2.0 tool.Aims
Methods
Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Aims
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