Aims. The success of anterior cruciate ligament reconstruction (ACLR)
depends on osseointegration at the graft-tunnel interface and intra-articular
ligamentization. Our aim was to conduct a systematic review of clinical
and preclinical studies that evaluated
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 meta-analysis was to assess the prognosis after early functional rehabilitation or traditional immobilization in patients who underwent operative or nonoperative treatment for rupture of the Achilles tendon. PubMed, Embase, Web of Science, and Cochrane Library were searched for randomized controlled trials (RCTs) from their inception to 3 June 2020, using keywords related to rupture of the Achilles tendon and rehabilitation. Data extraction was undertaken by independent reviewers and subgroup analyses were performed based on the form of treatment. Risk ratios (RRs) and weighted mean differences (WMDs) (with 95% confidence intervals (CIs)) were used as summary association measures.Aims
Methods
The aims of this study were to validate the outcome of total elbow arthroplasty (TEA) in patients with rheumatoid arthritis (RA), and to identify factors that affect the outcome. We searched PubMed, MEDLINE, Cochrane Reviews, and Embase from between January 2003 and March 2019. The primary aim was to determine the implant failure rate, the mode of failure, and risk factors predisposing to failure. A secondary aim was to identify the overall complication rate, associated risk factors, and clinical performance. A meta-regression analysis was completed to identify the association between each parameter with the outcome.Aims
Methods
The aim of this meta-analysis was to compare the outcome of total elbow arthroplasty (TEA) undertaken for rheumatoid arthritis (RA) with TEA performed for post-traumatic conditions with regard to implant failure, functional outcome, and perioperative complications. We completed a comprehensive literature search on PubMed, Web of Science, Embase, and the Cochrane Library and conducted a systematic review and meta-analysis. Nine cohort studies investigated the outcome of TEA between RA and post-traumatic conditions. The preferred reporting items for systematic reviews and meta-analysis (Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)) guidelines and Newcastle-Ottawa scale were applied to assess the quality of the included studies. We assessed three major outcome domains: implant failures (including aseptic loosening, septic loosening, bushing wear, axle failure, component disassembly, or component fracture); functional outcomes (including arc of range of movement, Mayo Elbow Performance Score (MEPS), and the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire), and perioperative complications (including deep infection, intraoperative fracture, postoperative fracture, and ulnar neuropathy).Aims
Materials and Methods
