Institution
University of Toronto
Education•Toronto, Ontario, Canada•
About: University of Toronto is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 126067 authors who have published 294940 publications receiving 13536856 citations. The organization is also known as: UToronto & U of T.
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University of Bristol1, University Hospitals Bristol NHS Foundation Trust2, Monash University3, French Institute of Health and Medical Research4, Cochrane Collaboration5, Paris Descartes University6, St George's, University of London7, University of York8, Queen Mary University of London9, Clinical Trial Service Unit10, Harvard University11, University of Oxford12, University of Southern Denmark13, Odense University Hospital14, University of Alberta15, University of Toronto16, University of Manchester17, Johns Hopkins University18, McGill University19, University College London20
TL;DR: The Cochrane risk-of-bias tool has been updated to respond to developments in understanding how bias arises in randomised trials, and to address user feedback on and limitations of the original tool.
Abstract: Assessment of risk of bias is regarded as an essential component of a systematic review on the effects of an intervention. The most commonly used tool for randomised trials is the Cochrane risk-of-bias tool. We updated the tool to respond to developments in understanding how bias arises in randomised trials, and to address user feedback on and limitations of the original tool.
9,228 citations
Cooper University Hospital1, St George's Hospital2, Memorial Hospital of Rhode Island3, Emory University4, University of Colorado Denver5, McMaster University6, Washington University in St. Louis7, University of Chicago8, University of Jena9, Rush University Medical Center10, University of Pittsburgh11, University of Pennsylvania12, Federal University of São Paulo13, University of Toronto14, Royal Perth Hospital15, Guy's and St Thomas' NHS Foundation Trust16, Université libre de Bruxelles17
TL;DR: An update to the “Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock,” last published in 2008 is provided.
Abstract: Objective:To provide an update to the “Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock,” last published in 2008.Design:A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at ke
9,137 citations
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Abstract: Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
9,091 citations
University of Amsterdam1, University of Toronto2, Centre Hospitalier Universitaire de Toulouse3, Cleveland Clinic4, Tohoku University5, Charles University in Prague6, University College Dublin7, University of Basel8, Icahn School of Medicine at Mount Sinai9, Lund University10, University College London11, University of California, San Francisco12, Mayo Clinic13, University of Texas Health Science Center at Houston14
TL;DR: These revisions simplify the McDonald Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.
Abstract: New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.
8,883 citations
TL;DR: This article argued that social identification is a perception of oneness with a group of persons, and social identification stems from the categorization of individuals, the distinctiveness and prestige of the group, the salience of outgroups, and the factors that traditionally are associated with group formation.
Abstract: It is argued that (a) social identification is a perception of oneness with a group of persons; (b) social identification stems from the categorization of individuals, the distinctiveness and prestige of the group, the salience of outgroups, and the factors that traditionally are associated with group formation; and (c) social identification leads to activities that are congruent with the identity, support for institutions that embody the identity, stereotypical perceptions of self and others, and outcomes that traditionally are associated with group formation, and it reinforces the antecedents of identification. This perspective is applied to organizational socialization, role conflict, and intergroup relations.
8,480 citations
Authors
Showing all 127245 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Gordon H. Guyatt | 231 | 1620 | 228631 |
| David J. Hunter | 213 | 1836 | 207050 |
| Rakesh K. Jain | 200 | 1467 | 177727 |
| Thomas C. Südhof | 191 | 653 | 118007 |
| Gordon B. Mills | 187 | 1273 | 186451 |
| George Efstathiou | 187 | 637 | 156228 |
| John P. A. Ioannidis | 185 | 1311 | 193612 |
| Paul M. Thompson | 183 | 2271 | 146736 |
| Yusuke Nakamura | 179 | 2076 | 160313 |
| Chris Sander | 178 | 713 | 233287 |
| David R. Williams | 178 | 2034 | 138789 |
| David L. Kaplan | 177 | 1944 | 146082 |
| Jasvinder A. Singh | 176 | 2382 | 223370 |
| Hyun-Chul Kim | 176 | 4076 | 183227 |
| Deborah J. Cook | 173 | 907 | 148928 |