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Institution

University of Virginia

EducationCharlottesville, Virginia, United States
About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.


Papers
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Journal ArticleDOI
TL;DR: The Surviving Sepsis Campaign CO VID-19 panel issued several recommendations to help support healthcare workers caring for critically ill ICU patients with COVID-19, and will provide new recommendations in further releases of these guidelines.
Abstract: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, Coronavirus Disease 2019 (COVID-19), affecting thousands of people around the world. Urgent guidance for clinicians caring for the sickest of these patients is needed. We formed a panel of 36 experts from 12 countries. All panel members completed the World Health Organization conflict of interest disclosure form. The panel proposed 53 questions that are relevant to the management of COVID-19 in the ICU. We searched the literature for direct and indirect evidence on the management of COVID-19 in critically ill patients in the ICU. We identified relevant and recent systematic reviews on most questions relating to supportive care. We assessed the certainty in the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, then generated recommendations based on the balance between benefit and harm, resource and cost implications, equity, and feasibility. Recommendations were either strong or weak, or in the form of best practice recommendations. The Surviving Sepsis Campaign COVID-19 panel issued 54 statements, of which 4 are best practice statements, 9 are strong recommendations, and 35 are weak recommendations. No recommendation was provided for 6 questions. The topics were: (1) infection control, (2) laboratory diagnosis and specimens, (3) hemodynamic support, (4) ventilatory support, and (5) COVID-19 therapy. The Surviving Sepsis Campaign COVID-19 panel issued several recommendations to help support healthcare workers caring for critically ill ICU patients with COVID-19. When available, we will provide new recommendations in further releases of these guidelines.

1,762 citations

Journal ArticleDOI
TL;DR: Among patients with heart failure and moderate‐to‐severe or severe secondary mitral regurgitation who remained symptomatic despite the use of maximal doses of guideline‐directed medical therapy, transcatheter mitral‐valve repair resulted in a lower rate of hospitalization forHeart failure and lower all‐cause mortality within 24 months of follow‐up than medical therapy alone.
Abstract: Background Among patients with heart failure who have mitral regurgitation due to left ventricular dysfunction, the prognosis is poor Transcatheter mitral-valve repair may improve their clinical outcomes Methods At 78 sites in the United States and Canada, we enrolled patients with heart failure and moderate-to-severe or severe secondary mitral regurgitation who remained symptomatic despite the use of maximal doses of guideline-directed medical therapy Patients were randomly assigned to transcatheter mitral-valve repair plus medical therapy (device group) or medical therapy alone (control group) The primary effectiveness end point was all hospitalizations for heart failure within 24 months of follow-up The primary safety end point was freedom from device-related complications at 12 months; the rate for this end point was compared with a prespecified objective performance goal of 880% Results Of the 614 patients who were enrolled in the trial, 302 were assigned to the device group and 312 t

1,758 citations

Journal ArticleDOI
TL;DR: The authors compared Mechanical Turk participants with community and student samples on a set of personality dimensions and classic decision-making biases and found that MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains.
Abstract: Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: they are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results. Copyright © 2012 John Wiley & Sons, Ltd.

1,755 citations

Journal ArticleDOI
04 Jan 2008-Science
TL;DR: This comparison reveals genomic changes concomitant with the evolutionary movement to land, including a general increase in gene family complexity; loss of genes associated with aquatic environments; acquisition of genes for tolerating terrestrial stresses; and the development of the auxin and abscisic acid signaling pathways for coordinating multicellular growth and dehydration response.
Abstract: We report the draft genome sequence of the model moss Physcomitrella patens and compare its features with those of flowering plants, from which it is separated by more than 400 million years, and unicellular aquatic algae. This comparison reveals genomic changes concomitant with the evolutionary movement to land, including a general increase in gene family complexity; loss of genes associated with aquatic environments (e.g., flagellar arms); acquisition of genes for tolerating terrestrial stresses (e.g., variation in temperature and water availability); and the development of the auxin and abscisic acid signaling pathways for coordinating multicellular growth and dehydration response. The Physcomitrella genome provides a resource for phylogenetic inferences about gene function and for experimental analysis of plant processes through this plant's unique facility for reverse genetics.

1,749 citations

Journal ArticleDOI
TL;DR: The focus of this paper is aircraft and aircraft engines but the broader focus is on the role of materials in creating lightweight structures, and there are examples used that are relevant to automotive applications once they are adjusted for cost.

1,746 citations


Authors

Showing all 53083 results

NameH-indexPapersCitations
Joan Massagué189408149951
Michael Rutter188676151592
Gordon B. Mills1871273186451
Ralph Weissleder1841160142508
Gonçalo R. Abecasis179595230323
Jie Zhang1784857221720
John R. Yates1771036129029
John A. Rogers1771341127390
Bradley Cox1692150156200
Mika Kivimäki1661515141468
Hongfang Liu1662356156290
Carl W. Cotman165809105323
Ralph A. DeFronzo160759132993
Elio Riboli1581136110499
Dan R. Littman157426107164
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023189
2022783
20215,565
20205,600
20195,001
20184,586