Institution
Monash University
Education•Melbourne, Victoria, Australia•
About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.
Papers published on a yearly basis
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Institute for Health Metrics and Evaluation1, College of Health Sciences, Bahrain2, Harvard University3, Kwame Nkrumah University of Science and Technology4, Charité5, Ahmadu Bello University6, University of the Philippines Manila7, Pontifical Xavierian University8, Madawalabu University9, World Bank10, Public Health Foundation of India11, Guy's and St Thomas' NHS Foundation Trust12, Griffith University13, University of New South Wales14, Massey University15, University of Peradeniya16, University of Sydney17, Chinese Center for Disease Control and Prevention18, Russian Academy of Sciences19, Tehran University of Medical Sciences20, Auckland University of Technology21, James Cook University22, Monash University23, University of California, San Francisco24, Arabian Gulf University25, Central South University26, Virginia Commonwealth University27, Jordan University of Science and Technology28, Health Services Academy29, Oregon Health & Science University30, University of Sheffield31, University at Albany, SUNY32, Aintree University Hospitals NHS Foundation Trust33, Swansea University34, University of York35, South African Medical Research Council36, Children's Hospital of Philadelphia37, Addis Ababa University38, Curtin University39, University of Washington40, Queensland University of Technology41, University of British Columbia42, Suez Canal University43, Karolinska Institutet44, University of Alabama at Birmingham45, An-Najah National University46, Tufts Medical Center47, Norwegian Institute of Public Health48, Stavanger University Hospital49, University of Cape Town50, University of California, Irvine51, University of Illinois at Urbana–Champaign52, St. John's University53, Johns Hopkins University54, Hanoi Medical University55, National Research University – Higher School of Economics56, University of Gondar57, University of Hong Kong58, Jackson State University59, Wuhan University60
TL;DR: An overview of injury estimates from the 2013 update of GBD is provided, with detailed information on incidence, mortality, DALYs and rates of change from 1990 to 2013 for 26 causes of injury, globally, by region and by country.
Abstract: Background The Global Burden of Diseases (GBD), Injuries, and Risk Factors study used the disability-adjusted life year (DALY) to quantify the burden of diseases, injuries, and risk factors. This paper provides an overview of injury estimates from the 2013 update of GBD, with detailed information on incidence, mortality, DALYs and rates of change from 1990 to 2013 for 26 causes of injury, globally, by region and by country.
Methods Injury mortality was estimated using the extensive GBD mortality database, corrections for ill-defined cause of death and the cause of death ensemble modelling tool. Morbidity estimation was based on inpatient and outpatient data sets, 26 cause-of-injury and 47 nature-of-injury categories, and seven follow-up studies with patient-reported long-term outcome measures.
Results In 2013, 973 million (uncertainty interval (UI) 942 to 993) people sustained injuries that warranted some type of healthcare and 4.8 million (UI 4.5 to 5.1) people died from injuries. Between 1990 and 2013 the global age-standardised injury DALY rate decreased by 31% (UI 26% to 35%). The rate of decline in DALY rates was significant for 22 cause-of-injury categories, including all the major injuries.
Conclusions Injuries continue to be an important cause of morbidity and mortality in the developed and developing world. The decline in rates for almost all injuries is so prominent that it warrants a general statement that the world is becoming a safer place to live in. However, the patterns vary widely by cause, age, sex, region and time and there are still large improvements that need to be made.
883 citations
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TL;DR: Evidence is provided that clinically acceptable errors are possible in gait analysis, andVariability between studies, however, suggests that they are not always achieved.
882 citations
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University of Ottawa1, World Health Organization2, University of Pittsburgh3, King Saud bin Abdulaziz University for Health Sciences4, University of Edinburgh5, University of Jena6, Utrecht University7, Oswaldo Cruz Foundation8, Monash University9, Public Health England10, Liverpool School of Tropical Medicine11, University of Liverpool12, University of Oxford13, The Chinese University of Hong Kong14, Imperial College London15, Sungkyunkwan University16, Trinity College, Dublin17, Queen's University Belfast18, Johns Hopkins University19, Radboud University Nijmegen20, University of Bonn21, Seoul National University22, University of Brescia23, Beijing University of Chinese Medicine24, Centers for Disease Control and Prevention25, Tianjin University of Traditional Chinese Medicine26
TL;DR: A minimum set of common outcome measures for studies of COVID-19, which includes a measure of viral burden, patient survival, and patient progression through the health-care system by use of the WHO Clinical Progression Scale are urged.
Abstract: Summary Clinical research is necessary for an effective response to an emerging infectious disease outbreak. However, research efforts are often hastily organised and done using various research tools, with the result that pooling data across studies is challenging. In response to the needs of the rapidly evolving COVID-19 outbreak, the Clinical Characterisation and Management Working Group of the WHO Research and Development Blueprint programme, the International Forum for Acute Care Trialists, and the International Severe Acute Respiratory and Emerging Infections Consortium have developed a minimum set of common outcome measures for studies of COVID-19. This set includes three elements: a measure of viral burden (quantitative PCR or cycle threshold), a measure of patient survival (mortality at hospital discharge or at 60 days), and a measure of patient progression through the health-care system by use of the WHO Clinical Progression Scale, which reflects patient trajectory and resource use over the course of clinical illness. We urge investigators to include these key data elements in ongoing and future studies to expedite the pooling of data during this immediate threat, and to hone a tool for future needs.
882 citations
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Monash University1, Kaiser Permanente2, Pennington Biomedical Research Center3, Copenhagen University Hospital4, Ewha Womans University5, Norwegian Institute of Public Health6, Michigan State University7, Dankook University8, University of Antwerp9, Katholieke Universiteit Leuven10, University of California, Irvine11
TL;DR: More than 1 million pregnant women had gestational weight gain greater than or less than guideline recommendations, compared with weight gain within recommended levels, was associated with higher risk of adverse maternal and infant outcomes.
Abstract: Importance Body mass index (BMI) and gestational weight gain are increasing globally. In 2009, the Institute of Medicine (IOM) provided specific recommendations regarding the ideal gestational weight gain. However, the association between gestational weight gain consistent with theIOM guidelines and pregnancy outcomes is unclear. Objective To perform a systematic review, meta-analysis, and metaregression to evaluate associations between gestational weight gain above or below the IOM guidelines (gain of 12.5-18 kg for underweight women [BMI Data Sources and Study Selection Search of EMBASE, Evidence-Based Medicine Reviews, MEDLINE, and MEDLINE In-Process between January 1, 1999, and February 7, 2017, for observational studies stratified by prepregnancy BMI category and total gestational weight gain. Data Extraction and Synthesis Data were extracted by 2 independent reviewers. Odds ratios (ORs) and absolute risk differences (ARDs) per live birth were calculated using a random-effects model based on a subset of studies with available data. Main Outcomes and Measures Primary outcomes were small for gestational age (SGA), preterm birth, and large for gestational age (LGA). Secondary outcomes were macrosomia, cesarean delivery, and gestational diabetes mellitus. Results Of 5354 identified studies, 23 (n = 1 309 136 women) met inclusion criteria. Gestational weight gain was below or above guidelines in 23% and 47% of pregnancies, respectively. Gestational weight gain below the recommendations was associated with higher risk of SGA (OR, 1.53 [95% CI, 1.44-1.64]; ARD, 5% [95% CI, 4%-6%]) and preterm birth (OR, 1.70 [1.32-2.20]; ARD, 5% [3%-8%]) and lower risk of LGA (OR, 0.59 [0.55-0.64]; ARD, −2% [−10% to −6%]) and macrosomia (OR, 0.60 [0.52-0.68]; ARD, −2% [−3% to −1%]); cesarean delivery showed no significant difference (OR, 0.98 [0.96-1.02]; ARD, 0% [−2% to 1%]). Gestational weight gain above the recommendations was associated with lower risk of SGA (OR, 0.66 [0.63-0.69]; ARD, −3%; [−4% to −2%]) and preterm birth (OR, 0.77 [0.69-0.86]; ARD, −2% [−2% to −1%]) and higher risk of LGA (OR, 1.85 [1.76-1.95]; ARD, 4% [2%-5%]), macrosomia (OR, 1.95 [1.79-2.11]; ARD, 6% [4%-9%]), and cesarean delivery (OR, 1.30 [1.25-1.35]; ARD, 4% [3%-6%]). Gestational diabetes mellitus could not be evaluated because of the nature of available data. Conclusions and Relevance In this systematic review and meta-analysis of more than 1 million pregnant women, 47% had gestational weight gain greater than IOM recommendations and 23% had gestational weight gain less than IOM recommendations. Gestational weight gain greater than or less than guideline recommendations, compared with weight gain within recommended levels, was associated with higher risk of adverse maternal and infant outcomes.
881 citations
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California Institute of Technology1, SRI International2, University of Edinburgh3, Leibniz Association4, Martin Luther University of Halle-Wittenberg5, Memorial Sloan Kettering Cancer Center6, University of Hertfordshire7, National Institutes of Health8, University of Auckland9, University of Washington10, Heidelberg University11, University of Manchester12, Monash University13, Mines ParisTech14, University of British Columbia15, Bilkent University16, Keio University17, Ontario Institute for Cancer Research18, Stellenbosch University19, Okinawa Institute of Science and Technology20
TL;DR: The Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists, believes that it will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge.
Abstract: Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
880 citations
Authors
Showing all 36568 results
Name | H-index | Papers | Citations |
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Bert Vogelstein | 247 | 757 | 332094 |
Kenneth W. Kinzler | 215 | 640 | 243944 |
David J. Hunter | 213 | 1836 | 207050 |
David R. Williams | 178 | 2034 | 138789 |
Yang Yang | 171 | 2644 | 153049 |
Lei Jiang | 170 | 2244 | 135205 |
Dongyuan Zhao | 160 | 872 | 106451 |
Christopher J. O'Donnell | 159 | 869 | 126278 |
Leif Groop | 158 | 919 | 136056 |
Mark E. Cooper | 158 | 1463 | 124887 |
Theo Vos | 156 | 502 | 186409 |
Mark J. Smyth | 153 | 713 | 88783 |
Rinaldo Bellomo | 147 | 1714 | 120052 |
Detlef Weigel | 142 | 516 | 84670 |
Geoffrey Burnstock | 141 | 1488 | 99525 |