3,786 research outputs found

    Dexketoprofen trometamol in the acute treatment of migraine attack: A phase ii, randomized, double-blind, crossover, placebo-controlled, dose optimization study

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    Migraine is a disabling disease that can significantly affect a person's quality of life. This study assessed the efficacy and tolerability of the 2 doses of dexketoprofen trometamol (DKP) compared to placebo for migraine treatment. Ninety-three patients with at least 1 migraine attack per month in the preceding 6 months were enrolled and randomized to 25 mg DKP, 50 mg DKP, and placebo in a randomized, double-blind, single-center, crossover, placebo-controlled study. Primary endpoint was pain-free episodes 2 hours after drug intake. The presence of accompanying symptoms and adverse effects was also recorded. Seventy-six patients (mean age 40.5 ± 10.9 and 61% female) completed the study. At baseline, mean number of attacks/month was 3.7 ± 1.3, with a mean duration of 15.4 ± 13.5 hours. Prevalence of pain-free episodes after drug intake was significantly reduced by 50 mg DKP vs placebo (33.8 vs 14.7%, P = .0065) whereas the dose of DKP 25 mg was better than placebo but did not reach statistical significance (23 vs 14.7%, P = .1182). Both 25 mg DKP (56.8 vs 25.3%, P = .0002) and 50 mg DKP improved headache relief compared to placebo. Furthermore, both doses of DKP increased the absence of functional disability (25 mg DKP, 39.7 vs 24%, P = .045; and 50 mg DKP, 45.9 vs 24%, P < .0004). Both doses of DKP were effective and well tolerated for acute migraine treatment

    An Unsupervised Approach for Automotive Driver Identification

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    The adoption of on-vehicle monitoring devices allows different entities to gather valuable data about driving styles, which can be further used to infer a variety of information for different purposes, such as fraud detection and driver profiling. In this paper, we focus on the identification of the number of people usually driving the same vehicle, proposing a data analytic work-flow specifically designed to address this problem. Our approach is based on unsupervised learning algorithms working on non-invasive data gathered from a specialized embedded device. In addition, we present a preliminary evaluation of our approach, showing promising driver identification capabilities and a limited computational effort
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