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https://www.selleckchem.com/products/tpx-0005.html It resulted in a coefficient of determination (r2) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors. To systematically review the effectiveness of electromyographic biofeedback interventions to improve pain and function of patients with shoulder pain. Systematic review of controlled clinical trials. Databases (Medline, EMBASE, CINAHL, PEDro, CENTRAL, Web of Science, and SCOPUS) were searched in December 2020. Randomized clinical trials that investigated the effects of electromyographic biofeedback for individuals with shoulder pain. Patient-reported pain and functional outcomes were collected and synthesized. The level of evidence was synthesized using GRADE and Standardized Mean Differences and 95% confidence interval were calculated using a random-effects inverse variance model for meta-analysis. Five studies were included with a total sample of 272 individuals with shoulder pain. Very-low quality of evidence indicated that electromyographic biofeedback was not superior to control for reducing shoulder pain (standardized mean differences = -0.21, 95% confidence interval -0.67 to 0.24,  = 0.36). Very-low quality of evidence indicated that electromyographic biofeedback interventions were not superior to control for improving shoulder function (standardized mean differences = -0.11, 95% confidence interval -0.41 to 0.19,  = 0.48). Electromyographic biofeedback may be not effective for improving shoulder pain and function. However, the limited number of included studies and very low quality of evidence does not support a definitive recommendation about the effectiveness of electromyographic biofeedback to treat individuals with shoulder pain. Electromyographic biofeedback may be not effective for improving shoulder pain and function. However, the limited number of included studies

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