For displaced femoral neck fractures (FNFs) in geriatric patients, there remains uncertainty regarding the effect of total hip arthroplasty (THA) compared with hemiarthroplasty (HA) in the guidelines. We aimed to compare 90-day surgical readmission, in-hospital complications, and charges between THA and HA in these patients. The Hospital Quality Monitoring System was queried from 1 January 2013 to 31 December 2019 for displaced FNFs in geriatric patients treated with THA or HA. After propensity score matching, which identified 33,849 paired patients, outcomes were compared between THA and HA using logistic and linear regression models.Aims
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To evaluate inducing osteoarthritis (OA) by surgical destabilization of the medial meniscus (DMM) in mice with and without a stereomicroscope. Based on sample size calculation, 70 male C57BL/6 mice were randomly assigned to three surgery groups: DMM aided by a stereomicroscope; DMM by naked eye; or sham surgery. The group information was blinded to researchers. Mice underwent static weightbearing, von Frey test, and gait analysis at two-week intervals from eight to 16 weeks after surgery. Histological grade of OA was determined with the Osteoarthritis Research Society International (OARSI) scoring system.Aims
Methods
Treatment outcomes for methicillin-resistant Total knee arthroplasty (TKA), MRSA inoculation, debridement, and vancomycin-spacer implantation were performed successively in rats to mimic first-stage PJI during the two-stage revision arthroplasty procedure. Vancomycin was administered intraperitoneally or intra-articularly for two weeks to control the infection after debridement and spacer implantation.Aims
Methods
Automated identification of arthroplasty implants could aid in pre-operative planning and is a task which could be facilitated through artificial intelligence (AI) and deep learning. The purpose of this study was to develop and test the performance of a deep learning system (DLS) for automated identification and classification of knee arthroplasty (KA) on radiographs. We collected 237 AP knee radiographs with equal proportions of native knees, total KA (TKA), and unicompartmental KA (UKA), as well as 274 radiographs with equal proportions of Smith & Nephew Journey and Zimmer NexGen TKAs. Data augmentation was used to increase the number of images available for DLS development. These images were used to train, validate, and test deep convolutional neural networks (DCNN) to 1) detect the presence of TKA; 2) differentiate between TKA and UKA; and 3) differentiate between the 2 TKA models. Receiver operating characteristic (ROC) curves were generated with area under the curve (AUC) calculated to assess test performance.Introduction
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