https://www.selleckchem.com/pr....oducts/BMS-794833.ht
While younger and older adults can perform upper-limb reaches to spatial targets with comparable endpoint accuracy (i.e., Helsen et al., 2016; Goodman et al., 202, movement planning (i.e., reaction time) is significantly longer in older versus younger adults (e.g., Pohl et al., 1996; Goodman et al., 202. Critically relevant to the current study, age-related differences in reaction time are even greater when older adults plan movement towards somatosensory versus visual or bimodal targets in the absence of vision of the moving lim

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A sample of 590 patients was included in this study. Of them, 180 (30.5%) had favorable outcomes and 152 (25.8%) had miserable outcomes. All selected machine learning models outperformed the DRAGON score on accuracy of outcome prediction (Logistic Regression 0.70, Supportive Vector Machine 0.67, Random Forest 0.69, and Extreme Gradient Boosting 0.67, vs. DRAGON 0.51, p less then 0.001). Among all selected models, Logistic Regression also had a better performance than the DRAGON score on positive predictive value, sensitivity, and specifi