Aims. The aim of this study was to identify the origin and development of the threshold for surgical intervention, highlight the consequences of residual displacement, and justify the importance of accurate measurement. Methods. A systematic review of three databases was performed to establish the origin and adaptations of the threshold, with papers screened and relevant citations reviewed. This search identified papers investigating functional outcome, including presence of arthritis, following injury. Orthopaedic textbooks were reviewed to ensure no earlier mention of the threshold was present. Results. Knirk and Jupiter (1986) were the first to quantify a threshold, with all their patients developing arthritis with > 2 mm displacement. Some papers have discussed using 1 mm, although 2 mm is most widely reported. Current guidance from the British Society for Surgery of the Hand and a Delphi panel support 2 mm as an appropriate value. Although this paper is still widely cited, the authors published a re-examination of the data showing methodological flaws which is not as widely reported. They claim their conclusions are still relevant today; however, radiological arthritis does not correlate with the clinical presentation. Function following injury has been shown to be equivalent to an uninjured population, with arthritis progressing slowly or not at all.
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
Methods
The aim of this study was to report the pooled prevalence of post-traumatic osteoarthritis (PTOA) and examine whether the risk of developing PTOA after anterior cruciate ligament (ACL) injury has decreased in recent decades. The PubMed and Web of Science databases were searched from 1 January 1980 to 11 May 2022. Patient series, observational studies, and clinical trials having reported the prevalence of radiologically confirmed PTOA after ACL injury, with at least a ten-year follow-up, were included. All studies were analyzed simultaneously, and separate analyses of the operative and nonoperative knees were performed. The prevalence of PTOA was calculated separately for each study, and pooled prevalence was reported with 95% confidence intervals (CIs) using either a fixed or random effects model. To examine the effect of the year of injury on the prevalence, a logit transformed meta-regression analysis was used with a maximum-likelihood estimator. Results from meta-regression analyses were reported with the unstandardized coefficient (β).Aims
Methods
To systematically review the predominant complication rates and changes to patient-reported outcome measures (PROMs) following osteochondral allograft (OCA) transplantation for shoulder instability. This systematic review, following PRISMA guidelines and registered in PROSPERO, involved a comprehensive literature search using PubMed, Embase, Web of Science, and Scopus. Key search terms included “allograft”, “shoulder”, “humerus”, and “glenoid”. The review encompassed 37 studies with 456 patients, focusing on primary outcomes like failure rates and secondary outcomes such as PROMs and functional test results.Aims
Methods
This study aimed to answer two questions: what are the best diagnostic methods for diagnosing bacterial arthritis of a native joint?; and what are the most commonly used definitions for bacterial arthritis of a native joint? We performed a search of PubMed, Embase, and Cochrane libraries for relevant studies published between January 1980 and April 2020. Of 3,209 identified studies, we included 27 after full screening. Sensitivity, specificity, area under the curve, and Youden index of diagnostic tests were extracted from included studies. We grouped test characteristics per diagnostic modality. We extracted the definitions used to establish a definitive diagnosis of bacterial arthritis of a native joint per study.Aims
Methods
