Aims. This systematic review aims to compare the precision of component positioning, patient-reported outcome measures (PROMs), complications, survivorship, cost-effectiveness, and learning curves of MAKO robotic arm-assisted unicompartmental knee arthroplasty (RAUKA) with manual medial unicompartmental knee arthroplasty (mUKA). Methods. Searches of PubMed, MEDLINE, and Google Scholar were performed in November 2021 according to the Preferred Reporting Items for Systematic Review and Meta-­Analysis statement. Search terms included “robotic”, “unicompartmental”, “knee”, and “arthroplasty”. Published clinical research articles reporting the learning curves and cost-effectiveness of MAKO RAUKA, and those comparing the component precision, functional outcomes, survivorship, or complications with mUKA, were included for analysis. Results. A total of 179 articles were identified from initial screening, of which 14 articles satisfied the inclusion criteria and were included for analysis. The papers analyzed include one on learning curve, five on implant positioning, six on functional outcomes, five on complications, six on survivorship, and three on cost. The learning curve was six cases for operating time and zero for precision. There was consistent evidence of more precise implant positioning with MAKO RAUKA. Meta-analysis demonstrated lower overall complication rates associated with MAKO RAUKA (OR 2.18 (95% confidence interval (CI) 1.06 to 4.49); p = 0.040) but no difference in re-intervention, infection, Knee Society Score (KSS; mean difference 1.64 (95% CI -3.00 to 6.27); p = 0.490), or Western Ontario and McMaster Universities Arthritis Index (WOMAC) score (mean difference -0.58 (95% CI -3.55 to 2.38); p = 0.700). MAKO RAUKA was shown to be a cost-effective procedure, but this was directly related to volume. Conclusion. MAKO RAUKA was associated with improved precision of component positioning but was not associated with improved PROMs using the KSS and WOMAC scores. Future longer-term studies should report functional outcomes, potentially using scores with minimal ceiling effects and survival to assess whether the improved precision of MAKO RAUKA results in better outcomes. Cite this article: Bone Joint J 2022;104-B(5):541–548

Aims. The aims of this systematic review were to assess the learning curve of semi-active robotic arm-assisted total hip arthroplasty (rTHA), and to compare the accuracy, patient-reported functional outcomes, complications, and survivorship between rTHA and manual total hip arthroplasty (mTHA). Methods. Searches of PubMed, Medline, and Google Scholar were performed in April 2020 in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “hip”, and “arthroplasty”. The criteria for inclusion were published clinical research articles reporting the learning curve for rTHA (robotic arm-assisted only) and those comparing the implantation accuracy, functional outcomes, survivorship, or complications with mTHA. Results. There were 501 articles initially identified from databases and references. Following full text screening, 17 articles that satisfied the inclusion criteria were included. Four studies reported the learning curve of rTHA, 13 studies reported on implant positioning, five on functional outcomes, ten on complications, and four on survivorship. The meta-analysis showed a significantly greater number of cases of acetabular component placement in the safe zone compared with the mTHA group (95% confidence interval (CI) 4.10 to 7.94; p < 0.001) and that rTHA resulted in a significantly better Harris Hip Score compared to mTHA in the short- to mid-term follow-up (95% CI 0.46 to 5.64; p = 0.020). However, there was no difference in infection rates, dislocation rates, overall complication rates, and survival rates at short-term follow-up. Conclusion. The learning curve of rTHA was between 12 and 35 cases, which was dependent on the assessment goal, such as operating time, accuracy, and team working. Robotic arm-assisted total hip arthroplasty was associated with improved accuracy of component positioning and functional outcome, however no difference in complication rates or survival were observed at short- to mid-term follow-up. Overall, there remains an absence of high-quality level I evidence and cost analysis comparing rTHA and mTHA. Cite this article: Bone Joint J 2021;103-B(6):1009–1020

Aims

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.

Aims

Perthes' disease (PD) is a relatively rare syndrome of idiopathic osteonecrosis of the proximal femoral epiphysis. Treatment for Perthes' disease is controversial due to the many options available, with no clear superiority of one treatment over another. Despite having few evidence-based approaches, many patients with Perthes' disease are managed surgically. Positive outcome reporting, defined as reporting a study variable producing statistically significant positive (beneficial) results, is a phenomenon that can be considered a proxy for the strength of science. This study aims to conduct a systematic literature review with the hypothesis that positive outcome reporting is frequent in studies on the treatment of Perthes' disease.