Institution
University of Amsterdam
Education•Amsterdam, Noord-Holland, Netherlands•
About: University of Amsterdam is a education organization based out in Amsterdam, Noord-Holland, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 59309 authors who have published 140894 publications receiving 5984137 citations. The organization is also known as: UvA & Universiteit van Amsterdam.
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TL;DR: Results showed that negotiators were less contentious, engaged in more problem solving, and achieved higher joint outcomes when they had a prosocial rather than egoistic motive, but only when resistance to yielding was high (or unknown) rather than low.
Abstract: A meta-analysis of 28 studies examined support for the Theory of Cooperation and Competition (M. Deutsch, 1973) and Dual Concern Theory (D. G. Pruitt & J. Z. Rubin, 1986). Effects of social motive (prosocial vs. egoistic) and resistance to yielding (high vs. low vs. unknown) on contenting, problem solving, and joint outcomes were examined. Consistent with Dual Concern Theory, results showed that negotiators were less contentious, engaged in more problem solving, and achieved higher joint outcomes when they had a prosocial rather than egoistic motive, but only when resistance to yielding was high (or unknown) rather than low. The authors also explored the moderating effects of study characteristics and found effects for participation inducement (class exercise, participant pool), for publication status, and for treatment of no-agreement dyads.
655 citations
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Sahlgrenska University Hospital1, National Institutes of Health2, University of Paris3, Children's Hospital Oakland Research Institute4, University of Glasgow5, Centro Nacional de Investigaciones Cardiovasculares6, Aarhus University7, Medical University of Vienna8, University of Amsterdam9, University of California, Los Angeles10, University of Western Ontario11, Monash University12, University of Copenhagen13, Royal Perth Hospital14, University of Western Australia15, French Institute of Health and Medical Research16, Oregon Health & Science University17, University of Cambridge18, University of Bristol19, Trinity College, Dublin20, University of Texas Southwestern Medical Center21, Charité22, Utrecht University23, University of the Witwatersrand24, Imperial College London25, Technische Universität München26, University of Helsinki27, University of Groningen28, Hacettepe University29, University of Milan30, Columbia University31
TL;DR: In this paper, the authors proposed a method to solve the problem of the problem: this paper ] of "uniformity" of the distribution of data points in the data set.
Abstract: Abstract
655 citations
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TL;DR: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects.
Abstract: Importance Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensuskmeans clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures Derived phenotype (α, β, γ,and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P 33% chance of benefit to >60% chance of harm). Conclusions and Relevance In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.
655 citations
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TL;DR: It is proposed that scoring of the microcirculation should include an index of vascular density, assessment of capillary perfusion and a heterogeneity index, which is likely that image analysis software will ease analysis in the future.
Abstract: Microvascular alterations may play an important role in the development of organ failure in critically ill patients and especially in sepsis. Recent advances in technology have allowed visualization of the microcirculation, but several scoring systems have been used so it is sometimes difficult to compare studies. This paper reports the results of a round table conference that was organized in Amsterdam in November 2006 in order to achieve consensus on image acquisition and analysis. The participants convened to discuss the various aspects of image acquisition and the different scores, and a consensus statement was drafted using the Delphi methodology. The participants identified the following five key points for optimal image acquisition: five sites per organ, avoidance of pressure artifacts, elimination of secretions, adequate focus and contrast adjustment, and recording quality. The scores that can be used to describe numerically the microcirculatory images consist of the following: a measure of vessel density (total and perfused vessel density; two indices of perfusion of the vessels (proportion of perfused vessels and microcirculatory flow index); and a heterogeneity index. In addition, this information should be provided for all vessels and for small vessels (mostly capillaries) identified as smaller than 20 μm. Venular perfusion should be reported as a quality control index, because venules should always be perfused in the absence of pressure artifact. It is anticipated that although this information is currently obtained manually, it is likely that image analysis software will ease analysis in the future. We proposed that scoring of the microcirculation should include an index of vascular density, assessment of capillary perfusion and a heterogeneity index.
655 citations
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University of Oxford1, University of Cambridge2, Imperial College London3, French Alternative Energies and Atomic Energy Commission4, Wellcome Trust Sanger Institute5, Karolinska Institutet6, Karolinska University Hospital7, Uppsala University8, Leibniz Association9, University of Michigan10, Queen Mary University of London11, University of Leicester12, AstraZeneca13, University of Surrey14, University of Helsinki15, University of Amsterdam16, Harokopio University17, McGill University18, McMaster University19
TL;DR: Genome-wide association studies have identified 11 common variants convincingly associated with coronary artery disease (CAD), a modest number considering the apparent heritability of CAD(8) as mentioned in this paper.
Abstract: Genome-wide association studies have identified 11 common variants convincingly associated with coronary artery disease (CAD)(1-7), a modest number considering the apparent heritability of CAD(8). ...
654 citations
Authors
Showing all 59759 results
Name | H-index | Papers | Citations |
---|---|---|---|
Richard A. Flavell | 231 | 1328 | 205119 |
Scott M. Grundy | 187 | 841 | 231821 |
Stuart H. Orkin | 186 | 715 | 112182 |
Kenneth C. Anderson | 178 | 1138 | 126072 |
David A. Weitz | 178 | 1038 | 114182 |
Dorret I. Boomsma | 176 | 1507 | 136353 |
Brenda W.J.H. Penninx | 170 | 1139 | 119082 |
Michael Kramer | 167 | 1713 | 127224 |
Nicholas J. White | 161 | 1352 | 104539 |
Lex M. Bouter | 158 | 767 | 103034 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Jerome I. Rotter | 156 | 1071 | 116296 |
David Cella | 156 | 1258 | 106402 |
David Eisenberg | 156 | 697 | 112460 |
Naveed Sattar | 155 | 1326 | 116368 |