Institution
Brown University
Education•Providence, Rhode Island, United States•
About: Brown University is a education organization based out in Providence, Rhode Island, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35778 authors who have published 90896 publications receiving 4471489 citations. The organization is also known as: brown.edu & Brown.
Papers published on a yearly basis
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TL;DR: The utility of the Karnofsky Performance Status Scale as a valuable research tool when employed by trained observers is suggested and the relationship of the KPS to longevity in a population of terminal cancer patients documents its predictive validity.
Abstract: The Karnofsky Performance Status Scale (KPS) is widely used to quantify the functional status of cancer patients. However, limited data exist documenting its reliability and validity. The KPS is used in the National Hospice Study (NHS) as both a study eligibility criterion and an outcome measure. As part of intensive training, interviewers were instructed in and tested on guidelines for determining the KPS levels of patients. After 4 months of field experience, interviewers were again tested based on narrative patient descriptions. The interrator reliability of 47 NHS interviewers was found to be 0.97. The construct validity of the KPS was analyzed, and the KPS was found to be strongly related (P less than 0.001) to two other independent measures of patient functioning. Finally, the relationship of the KPS to longevity (r = 0.30) in a population of terminal cancer patients documents its predictive validity. These findings suggest the utility of the KPS as a valuable research tool when employed by trained observers.
965 citations
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20 Aug 2002TL;DR: This paper presents Aurora, a new DBMS that is currently under construction at Brandeis University, Brown University, and M.I.T. and describes the basic system architecture, a stream-oriented set of operators, optimization tactics, and support for real-time operation.
Abstract: This paper introduces monitoring applications, which we will show differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS that is currently under construction at Brandeis University, Brown University, and M.I.T. We describe the basic system architecture, a stream-oriented set of operators, optimization tactics, and support for real-time operation.
963 citations
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University of Alaska Fairbanks1, Woods Hole Research Center2, University of California3, Marine Biological Laboratory4, University of Missouri5, Oregon State University6, National Center for Atmospheric Research7, Max Planck Society8, University of New Hampshire9, United States Geological Survey10, Cornell University11, Stanford University12, University of Colorado Boulder13, United States Forest Service14, Brown University15, Duke University16
TL;DR: In this article, the imbalance between gross primary production (GPP) and ecosystem respiration (ER) was defined as a measure of the amount of carbon being transferred from one ecosystem to another.
Abstract: Recent projectionsofclimatic change havefocused a great deal of scientific and public attention on patterns of carbon (C) cycling as well as its controls, particularly the factors that determine whether an ecosystem is a net source or sink of atmospheric carbon dioxide (CO2). Net ecosystem production (NEP), a central concept in C-cycling research, has been used by scientists to represent two different concepts. We propose that NEP be restricted to just one of its two original definitions—the imbalance between gross primary production (GPP) and ecosystem respiration (ER). We further propose that a new term—net ecosystem carbon balance (NECB)—be applied to the net rate of C accumulation in (or loss from [negative sign]) ecosystems. Net ecosystem carbon balance differs from NEP when C fluxesotherthanCfixationandrespiration occur,or when inorganic C enters or leaves in dissolved form. These fluxes include the leaching loss or lateral transfer of C from the ecosystem; the emission of volatile organic C, methane, and carbon monoxide; and the release of soot and CO2 from fire. Carbon fluxes in addition to NEP are particularly important determinants of NECB over long time scales. However, even over short time scales, they are important in ecosystems such as streams, estuaries, wetlands, and cities. Recent technological advances have led to a diversity of approaches to the measurement of C fluxes at different temporal and spatial scales. These approaches frequently capture different components of NEP or NECB and can therefore be compared across scales only by carefully specifying the
962 citations
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Institute for Health Metrics and Evaluation1, University of Louisville2, Kermanshah University of Medical Sciences3, Karolinska Institutet4, Centre for Mental Health5, University of Queensland6, Tehran University of Medical Sciences7, South African Medical Research Council8, University of KwaZulu-Natal9, University of Colorado Boulder10, University of California, Irvine11, Fred Hutchinson Cancer Research Center12, Montefiore Medical Center13, Northeastern University14, University of Alabama at Birmingham15, Brown University16, San Diego State University17, University of Melbourne18, Albert Einstein College of Medicine19, Cambridge Health Alliance20, Johns Hopkins University21, University of Pittsburgh22, University of Cape Town23, Marshall University24, Case Western Reserve University25, University of London26, University of Illinois at Urbana–Champaign27, National Drug and Alcohol Research Centre28, Harvard University29, University of California, San Diego30, Yale University31, Georgetown University32, Veterans Health Administration33, Jackson State University34, University of Massachusetts Boston35, State University of New York System36, Simmons College37, University of California, Los Angeles38, Charles R. Drew University of Medicine and Science39, University of Oxford40, Norwegian Institute of Public Health41, Curtin University42, Pacific Institute43, Heidelberg University44, Jimma University45, Northwestern University46, Washington University in St. Louis47, Howard University48, University of New Mexico49, University at Buffalo50, University of Washington51, University of South Florida52, Tufts University53, University of Rochester Medical Center54, Kosin University55, Central South University56, Michigan State University57, Ball State University58, Nova Southeastern University59, Dalhousie University60, Mayo Clinic61, University of British Columbia62, Ohio State University63, Baylor University64, Jagiellonian University Medical College65, Wrocław Medical University66, Dartmouth College67, University of Western Ontario68, Oregon Health & Science University69, Virginia Commonwealth University70, Columbia University71, University of Canberra72, Aga Khan University73
TL;DR: There are wide differences in the burden of disease at the state level and specific diseases and risk factors, such as drug use disorders, high BMI, poor diet, high fasting plasma glucose level, and alcohol use disorders are increasing and warrant increased attention.
Abstract: Introduction Several studies have measured health outcomes in the United States, but none have provided a comprehensive assessment of patterns of health by state. Objective To use the results of the Global Burden of Disease Study (GBD) to report trends in the burden of diseases, injuries, and risk factors at the state level from 1990 to 2016. Design and Setting A systematic analysis of published studies and available data sources estimates the burden of disease by age, sex, geography, and year. Main Outcomes and Measures Prevalence, incidence, mortality, life expectancy, healthy life expectancy (HALE), years of life lost (YLLs) due to premature mortality, years lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 333 causes and 84 risk factors with 95% uncertainty intervals (UIs) were computed. Results Between 1990 and 2016, overall death rates in the United States declined from 745.2 (95% UI, 740.6 to 749.8) per 100 000 persons to 578.0 (95% UI, 569.4 to 587.1) per 100 000 persons. The probability of death among adults aged 20 to 55 years declined in 31 states and Washington, DC from 1990 to 2016. In 2016, Hawaii had the highest life expectancy at birth (81.3 years) and Mississippi had the lowest (74.7 years), a 6.6-year difference. Minnesota had the highest HALE at birth (70.3 years), and West Virginia had the lowest (63.8 years), a 6.5-year difference. The leading causes of DALYs in the United States for 1990 and 2016 were ischemic heart disease and lung cancer, while the third leading cause in 1990 was low back pain, and the third leading cause in 2016 was chronic obstructive pulmonary disease. Opioid use disorders moved from the 11th leading cause of DALYs in 1990 to the 7th leading cause in 2016, representing a 74.5% (95% UI, 42.8% to 93.9%) change. In 2016, each of the following 6 risks individually accounted for more than 5% of risk-attributable DALYs: tobacco consumption, high body mass index (BMI), poor diet, alcohol and drug use, high fasting plasma glucose, and high blood pressure. Across all US states, the top risk factors in terms of attributable DALYs were due to 1 of the 3 following causes: tobacco consumption (32 states), high BMI (10 states), or alcohol and drug use (8 states). Conclusions and Relevance There are wide differences in the burden of disease at the state level. Specific diseases and risk factors, such as drug use disorders, high BMI, poor diet, high fasting plasma glucose level, and alcohol use disorders are increasing and warrant increased attention. These data can be used to inform national health priorities for research, clinical care, and policy.
962 citations
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TL;DR: A CGA, with or without screening, and with follow-up, should be used in older cancer patients, in order to detect unaddressed problems, improve their functional status, and possibly their survival.
Abstract: Background: As more and more cancers occur in elderly people, oncologists are increasingly confronted with the necessity of integrating geriatric parameters in the treatment of their patients. Methods: The International Society of Geriatric Oncology (SIOG) created a task force to review the evidence on the use of a comprehensive geriatric assessment (CGA) in cancer patients. A systematic review of the evidence was conducted. Results: Several biological and clinical correlates of aging have been identified. Their relative weight and clinical usefulness is still poorly defined. There is strong evidence that a CGA detects many problems missed by a regular assessment in general geriatric and in cancer patients. There is also strong evidence that a CGA improves function and reduces hospitalization in the elderly. There is heterogeneous evidence that it improves survival and that it is cost-effective. There is corroborative evidence from a few studies conducted in cancer patients. Screening tools exist and were successfully used in settings such as the emergency room, but globally were poorly tested. The article contains recommendations for the use of CGA in research and clinical care for older cancer patients. Conclusions: A CGA, with or without screening, and with follow-up, should be used in older cancer patients, in order to detect unaddressed problems, improve their functional status, and possibly their survival. The task force cannot recommend any specific tool or approach above others at this point and general geriatric experience should be used.
962 citations
Authors
Showing all 36143 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
Robert Langer | 281 | 2324 | 326306 |
Robert M. Califf | 196 | 1561 | 167961 |
Eric J. Topol | 193 | 1373 | 151025 |
Joan Massagué | 189 | 408 | 149951 |
Joseph Biederman | 179 | 1012 | 117440 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
James F. Sallis | 169 | 825 | 144836 |
Steven N. Blair | 165 | 879 | 132929 |
Charles M. Lieber | 165 | 521 | 132811 |
J. S. Lange | 160 | 2083 | 145919 |
Christopher J. O'Donnell | 159 | 869 | 126278 |
Charles M. Perou | 156 | 573 | 202951 |
David J. Mooney | 156 | 695 | 94172 |
Richard J. Davidson | 156 | 602 | 91414 |