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Institution

University of Amsterdam

EducationAmsterdam, 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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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
28 May 2019-JAMA
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

Journal ArticleDOI
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

Journal ArticleDOI
John F. Peden1, Jemma C. Hopewell1, Danish Saleheen2, John C. Chambers3, Jorg Hager4, Nicole Soranzo5, Rory Collins1, John Danesh2, Paul Elliott3, Martin Farrall1, Kathy Stirrups5, Weihua Zhang3, Anders Hamsten6, Anders Hamsten7, Sarah Parish1, Mark Lathrop4, Hugh Watkins1, Robert Clarke1, Panos Deloukas5, Jaspal S. Kooner3, Anuj Goel1, Halit Ongen1, Rona J. Strawbridge7, Rona J. Strawbridge6, Simon Heath4, Anders Mälarstig6, Anders Mälarstig7, Anna Helgadottir1, John Öhrvik7, John Öhrvik6, Muhammed Murtaza5, Simon C. Potter5, Sarah E. Hunt5, Marc Delepine4, Shapour Jalilzadeh1, Tomas Axelsson8, Ann-Christine Syvänen8, Rhian Gwilliam5, Suzannah Bumpstead5, Emma Gray5, Sarah Edkins5, Lasse Folkersen7, Lasse Folkersen6, Theodosios Kyriakou1, Anders Franco-Cereceda6, Anders Gabrielsen6, Udo Seedorf9, Per Eriksson7, Per Eriksson6, Alison Offer1, Louise Bowman1, Peter Sleight1, Jane Armitage1, Richard Peto1, Gonçalo R. Abecasis10, Nabeel Ahmed, Mark J. Caulfield11, Peter Donnelly1, Philippe Froguel3, Angad S. Kooner, Mark I. McCarthy1, Nilesh J. Samani12, James Scott3, Joban Sehmi3, Angela Silveira7, Angela Silveira6, Mai-Lis Hellénius6, Ferdinand M. van't Hooft6, Ferdinand M. van't Hooft7, Gunnar O Olsson13, Stephan Rust9, Gerd Assmann9, Simona Barlera, Gianni Tognoni, Maria Grazia Franzosi, Pamela Linksted1, Fiona Green14, Asif Rasheed, Moazzam Zaidi, Nabi Shah, Maria Samuel, Nadeem Hayat Mallick, Muhammad Azhar, Khan Shah Zaman, Abdus Samad, M. Ishaq, Ali Raza Gardezi, Fazal-ur-Rehman Memon, Philippe M. Frossard, Tim D. Spector, Leena Peltonen15, Leena Peltonen5, Markku S. Nieminen, Juha Sinisalo, Veikko Salomaa, Samuli Ripatti15, Derrick A Bennett1, Karin Leander6, Bruna Gigante6, Ulf de Faire6, Silvia Pietri, Francesca Gori, Roberto Marchioli, Suthesh Sivapalaratnam16, John J.P. Kastelein16, Mieke D. Trip16, Eirini V. Theodoraki17, George V. Dedoussis17, Engert Jc18, Salim Yusuf19, Sonia S. Anand19 
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

NameH-indexPapersCitations
Richard A. Flavell2311328205119
Scott M. Grundy187841231821
Stuart H. Orkin186715112182
Kenneth C. Anderson1781138126072
David A. Weitz1781038114182
Dorret I. Boomsma1761507136353
Brenda W.J.H. Penninx1701139119082
Michael Kramer1671713127224
Nicholas J. White1611352104539
Lex M. Bouter158767103034
Wolfgang Wagner1562342123391
Jerome I. Rotter1561071116296
David Cella1561258106402
David Eisenberg156697112460
Naveed Sattar1551326116368
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023198
2022699
20219,646
20208,532
20197,821
20186,407