Institution
Eli Lilly and Company
Company•Indianapolis, Indiana, United States•
About: Eli Lilly and Company is a company organization based out in Indianapolis, Indiana, United States. It is known for research contribution in the topics: Population & Agonist. The organization has 17826 authors who have published 22835 publications receiving 946714 citations. The organization is also known as: Eli Lily.
Topics: Population, Agonist, Insulin, Placebo, Olanzapine
Papers published on a yearly basis
Papers
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TL;DR: This paper presents the parallel reduction of a 38-item questionnaire, the Nottingham Health Profile (NHP), through the analysis of the responses of a sample of 9,419 individuals, and finds the Rasch model provides an alternative scaling methodology that enables the examination of the hierarchical structure, unidimensionality and additivity of HRQOL measures.
Abstract: Background
Although health-related quality of life (HRQOL) instruments may offer satisfactory results, their length often limits the extent to which they are actually applied in clinical practice. Efforts to develop short questionnaires have largely focused on reducing existing instruments. The approaches most frequently employed for this purpose rely on statistical procedures that are considered exponents of Classical Test Theory (CTT). Despite the popularity of CTT, two major conceptual limitations have been pointed out: the lack of an explicit ordered continuum of items that represent a unidimensional construct, and the lack of additivity of rating scale data. In contrast to the CTT approach, the Rasch model provides an alternative scaling methodology that enables the examination of the hierarchical structure, unidimensionality and additivity of HRQOL measures. METHODS: In order to empirically compare CTT and Rasch Analysis (RA) results, this paper presents the parallel reduction of a 38-item questionnaire, the Nottingham Health Profile (NHP), through the analysis of the responses of a sample of 9,419 individuals.
245 citations
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TL;DR: The results show that a high-fat diet (HFD; ~16 weeks) causes anxiety and anhedonic behaviors, and pharmacological blockade of the innate immune inflammasome system by repeated administration of an inhibitor of the purinergic P2X7 receptor blocks the anxiety caused by HFD.
245 citations
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TL;DR: This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.
244 citations
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Harvard University1, University of Rochester2, University of Washington3, Stanford University4, East Carolina University5, Oslo University Hospital6, Washington University in St. Louis7, French Institute of Health and Medical Research8, University of Kiel9, MetroHealth10, University of Maryland, Baltimore11, Tufts University12, Eli Lilly and Company13, Johnson & Johnson14, University of Florida15, Astellas Pharma16, National Institutes of Health17, Imperial College London18, Pfizer19, Columbia University20, Ceres21, University of Pittsburgh22, Purdue Pharma23, Rambam Health Care Campus24
TL;DR: Evidence is presented on the most promising phenotypic characteristics of patients that are most predictive of individual variation in analgesic treatment outcomes, and the measurement tools that are best suited to evaluate these characteristics.
Abstract: There is tremendous interpatient variability in the response to analgesic therapy (even for efficacious treatments), which can be the source of great frustration in clinical practice This has led to calls for "precision medicine" or personalized pain therapeutics (ie, empirically based algorithms that determine the optimal treatments, or treatment combinations, for individual patients) that would presumably improve both the clinical care of patients with pain and the success rates for putative analgesic drugs in phase 2 and 3 clinical trials However, before implementing this approach, the characteristics of individual patients or subgroups of patients that increase or decrease the response to a specific treatment need to be identified The challenge is to identify the measurable phenotypic characteristics of patients that are most predictive of individual variation in analgesic treatment outcomes, and the measurement tools that are best suited to evaluate these characteristics In this article, we present evidence on the most promising of these phenotypic characteristics for use in future research, including psychosocial factors, symptom characteristics, sleep patterns, responses to noxious stimulation, endogenous pain-modulatory processes, and response to pharmacologic challenge We provide evidence-based recommendations for core phenotyping domains and recommend measures of each domain
244 citations
Authors
Showing all 17866 results
Name | H-index | Papers | Citations |
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Mark J. Daly | 204 | 763 | 304452 |
Irving L. Weissman | 201 | 1141 | 172504 |
Eric J. Topol | 193 | 1373 | 151025 |
Tony Hunter | 175 | 593 | 124726 |
Xiang Zhang | 154 | 1733 | 117576 |
Jerrold M. Olefsky | 143 | 595 | 77356 |
Stephen F. Badylak | 133 | 530 | 57083 |
George A. Bray | 131 | 896 | 100975 |
Lloyd Paul Aiello | 131 | 506 | 85550 |
Levi A. Garraway | 129 | 366 | 99989 |
Mark Sullivan | 126 | 802 | 63916 |
James A. Russell | 124 | 1024 | 87929 |
Tony L. Yaksh | 123 | 806 | 60898 |
Elisabetta Dejana | 122 | 430 | 48254 |
Hagop S. Akiskal | 118 | 565 | 50869 |