A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
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Arthrofibrosis is a relatively common complication after joint injuries and surgery, particularly in the knee. The present study used a previously described and validated rabbit model to assess the biomechanical, histopathological, and molecular effects of the mast cell stabilizer ketotifen on surgically induced knee joint contractures in female rabbits. A group of 12 skeletally mature rabbits were randomly divided into two groups. One group received subcutaneous (SQ) saline, and a second group received SQ ketotifen injections. Biomechanical data were collected at eight, ten, 16, and 24 weeks. At the time of necropsy, posterior capsule tissue was collected for histopathological and gene expression analyses (messenger RNA (mRNA) and protein).Aims
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