Institution
University of Connecticut
Education•Storrs, Connecticut, United States•
About: University of Connecticut is a education organization based out in Storrs, Connecticut, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35297 authors who have published 81224 publications receiving 2952682 citations. The organization is also known as: UConn & Storrs Agricultural School.
Papers published on a yearly basis
Papers
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TL;DR: Statin-associated symptoms are important because they prompt dose reduction or discontinuation of these life-saving mediations, and management of SAS requires making the possible diagnosis, altering or discontinuing the statin treatment, and using alternative lipid-lowering therapy.
474 citations
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TL;DR: It is concluded that there is insufficient evidence to prove the etiologic role of CoQ10 deficiency in statin-associated myopathy and that large, well-designed clinical trials are required to address this issue.
474 citations
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TL;DR: This work generalized an existing scale, the Labeled Magnitude Scale (LMS), by placing the label "strongest imaginable sensation of any kind" at the top, and produced similar results suggesting that the gLMS is valid for taste comparisons across nontasters, medium tasters, and supertasters.
474 citations
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27 Aug 2014TL;DR: This book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains and explains how the theoretical aspects influence the hypothesisTesting and changepoint Detection problems as well as the design of algorithms.
Abstract: Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.
474 citations
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Veterans Health Administration1, University of California, Irvine2, National Institutes of Health3, Virginia Commonwealth University4, University of Colorado Denver5, University of Texas Southwestern Medical Center6, Saint Louis University7, University of Connecticut8, Carolinas Medical Center9, Harvard University10, University of Washington11, University of Southern California12, University of Michigan13
TL;DR: Patients with advanced chronic hepatitis C who achieved SVR had a marked reduction in death/liver transplantation, and in liver‐related morbidity/mortality, although they remain at risk for HCC.
472 citations
Authors
Showing all 35666 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Richard A. Flavell | 231 | 1328 | 205119 |
Ralph Weissleder | 184 | 1160 | 142508 |
Eric J. Nestler | 178 | 748 | 116947 |
David L. Kaplan | 177 | 1944 | 146082 |
Masayuki Yamamoto | 171 | 1576 | 123028 |
Mark Gerstein | 168 | 751 | 149578 |
Marc A. Pfeffer | 166 | 765 | 133043 |
Carl W. Cotman | 165 | 809 | 105323 |
Murray F. Brennan | 161 | 925 | 97087 |
Alfred L. Goldberg | 156 | 474 | 88296 |
Xiang Zhang | 154 | 1733 | 117576 |
Hakon Hakonarson | 152 | 968 | 101604 |
Christopher P. Cannon | 151 | 1118 | 108906 |
James M. Wilson | 150 | 1010 | 78686 |