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Institution

McMaster University

EducationHamilton, Ontario, Canada
About: McMaster University is a education organization based out in Hamilton, Ontario, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 41361 authors who have published 101269 publications receiving 4251422 citations.


Papers
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Journal ArticleDOI
TL;DR: The authors argue that clues to coherence automatically activate the problem solver's relevant mnemonic and semantic networks, and at that point, it is represented as a hunch or hypothesis.

603 citations

Journal ArticleDOI
30 Jul 2020-BMJ
TL;DR: Glucocorticoids probably reduce mortality and mechanical ventilation in patients with covid-19 compared with standard care and the effectiveness of most interventions is uncertain because most of the randomised controlled trials so far have been small and have important study limitations.
Abstract: Objective To compare the effects of treatments for coronavirus disease 2019 (covid-19). Design Living systematic review and network meta-analysis. Data sources WHO covid-19 database, a comprehensive multilingual source of global covid-19 literature, up to 1 March 2021 and six additional Chinese databases up to 20 February 2021. Studies identified as of 12 February 2021 were included in the analysis. Study selection Randomised clinical trials in which people with suspected, probable, or confirmed covid-19 were randomised to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles. Methods After duplicate data abstraction, a bayesian network meta-analysis was conducted. Risk of bias of the included studies was assessed using a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development, and evaluation (GRADE) approach. For each outcome, interventions were classified in groups from the most to the least beneficial or harmful following GRADE guidance. Results 196 trials enrolling 76 767 patients were included; 111 (56.6%) trials and 35 098 (45.72%) patients are new from the previous iteration; 113 (57.7%) trials evaluating treatments with at least 100 patients or 20 events met the threshold for inclusion in the analyses. Compared with standard care, corticosteroids probably reduce death (risk difference 20 fewer per 1000 patients, 95% credible interval 36 fewer to 3 fewer, moderate certainty), mechanical ventilation (25 fewer per 1000, 44 fewer to 1 fewer, moderate certainty), and increase the number of days free from mechanical ventilation (2.6 more, 0.3 more to 5.0 more, moderate certainty). Interleukin-6 inhibitors probably reduce mechanical ventilation (30 fewer per 1000, 46 fewer to 10 fewer, moderate certainty) and may reduce length of hospital stay (4.3 days fewer, 8.1 fewer to 0.5 fewer, low certainty), but whether or not they reduce mortality is uncertain (15 fewer per 1000, 30 fewer to 6 more, low certainty). Janus kinase inhibitors may reduce mortality (50 fewer per 1000, 84 fewer to no difference, low certainty), mechanical ventilation (46 fewer per 1000, 74 fewer to 5 fewer, low certainty), and duration of mechanical ventilation (3.8 days fewer, 7.5 fewer to 0.1 fewer, moderate certainty). The impact of remdesivir on mortality and most other outcomes is uncertain. The effects of ivermectin were rated as very low certainty for all critical outcomes, including mortality. In patients with non-severe disease, colchicine may reduce mortality (78 fewer per 1000, 110 fewer to 9 fewer, low certainty) and mechanical ventilation (57 fewer per 1000, 90 fewer to 3 more, low certainty). Azithromycin, hydroxychloroquine, lopinavir-ritonavir, and interferon-beta do not appear to reduce risk of death or have an effect on any other patient-important outcome. The certainty in effects for all other interventions was low or very low. Conclusion Corticosteroids and interleukin-6 inhibitors probably confer important benefits in patients with severe covid-19. Janus kinase inhibitors appear to have promising benefits, but certainty is low. Azithromycin, hydroxychloroquine, lopinavir-ritonavir, and interferon-beta do not appear to have any important benefits. Whether or not remdesivir, ivermectin, and other drugs confer any patient-important benefit remains uncertain. Systematic review registration This review was not registered. The protocol is publicly available in the supplementary material. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This is the fourth version of the original article published on 30 July 2020 (BMJ 2020;370:m2980), and previous versions can be found as data supplements. When citing this paper please consider adding the version number and date of access for clarity.

602 citations

Journal ArticleDOI
01 Sep 2000-Blood
TL;DR: The frequency of immune heparin-induced thrombocytopenia (HIT) varies among prospective studies, and among patients in whom HIT-IgG formed and who were administered UFH, the probability for HIT was higher among orthopedic patients than among cardiac patients.

601 citations

Journal ArticleDOI
TL;DR: The content validity, interrater reliability, and test–retest reliability of the CFCS for children with CP are reported.
Abstract: Individuals with cerebral palsy (CP) have sensorimotor and developmental issues that affect their daily lives by restricting their mobility, manipulation of objects, and/or communication.1 Within the framework of the World Health Organization’s International Classification of Functioning, Disability and Health (ICF),2,3 the Gross Motor Function Classification System (GMFCS)4 and the Manual Ability Classification System (MACS) for children with CP5 make it possible to classify mobility and handling objects respectively, at the ICF activity/participation level.6 However, no analogous classification of functional communication has been available for use in CP practice and research. The lack of a communication classification tool that is quick, reliable, valid, and easy to use limits the comparison of descriptive CP epidemiology studies as well as the interpretation and generalizability of CP treatment studies. Communication disorders can be described from several perspectives: body structure and function level, activity level, and participation level, as well as environmental and personal levels.2,3,7–13 Estimates of communication disorders in CP have varied from 31%14 to 88%.15 This wide range is partly a result of the lack of a consensus definition of communication disorders within CP research and practice. A recent study from a Norwegian CP registry reported that 51% of children with CP had speech problems as classified by ratings of ‘slightly indistinct’, ‘obviously indistinct’, ‘severely indistinct’, or ‘no speech’, including 19% who had ‘no speech’.16 This population-based estimate reporting indistinct or no speech may underestimate CP communication disorders as it may not capture other types of communication problems resulting from hearing or language impairments. However, reporting speech, language, and hearing difficulties simply suggests the range of associated impairments in CP, not the more pertinent daily-life issues of how well a child with CP communicates with family, friends, acquaintances, and strangers.13 The purpose of this study was to create and validate a communication function classification system (CFCS) for children with CP, for use by a wide variety of individuals interested in CP. This required a shift from the traditional focus on body structure and function (i.e. assessing components of speech, language, and hearing problems), to a focus on activity/participation, specifically the way in which to classify a person’s communication capacity within real-life situations.

601 citations

Journal ArticleDOI
TL;DR: This paper will explain the basics of factor analysis and provide some guidelines relating to how the results should be reported and how to interpret the results.
Abstract: Factor analysis is a technique which is designed to reveal whether or not the pattern of responses on a number of tests can be explained by a smaller number of underlying traits or factors. Similarly, it can be used to indicate whether or not the various items on a questionnaire can be grouped into a few clusters with each cluster reflecting a different construct. As with all multivariate statistical tests, it is quite powerful and can provide much information about the instruments being used. Similarly, there are many ways it can be abused and misinterpreted. This paper will explain the basics of factor analysis and provide some guidelines relating to how the results should be reported.

598 citations


Authors

Showing all 41721 results

NameH-indexPapersCitations
Salim Yusuf2311439252912
Gordon H. Guyatt2311620228631
Simon D. M. White189795231645
George Efstathiou187637156228
Stuart H. Orkin186715112182
Terrie E. Moffitt182594150609
John J.V. McMurray1781389184502
Jasvinder A. Singh1762382223370
Deborah J. Cook173907148928
Andrew P. McMahon16241590650
Jack Hirsh14673486332
Holger J. Schünemann141810113169
John A. Peacock140565125416
David Price138168793535
Graeme J. Hankey137844143373
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023168
2022521
20216,351
20205,747
20195,093
20184,604