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      Meta-Analysis of Correlations between Altmetric Attention Score and Citations in Health Sciences

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          Abstract

          Introduction

          In recent years, several controversial reports of the correlation between altmetric score and citations have been published (range: -0.2 to 0.8). We conducted a meta-analysis to provide an in-depth statistical analysis of the correlation between altmetric score and number of citations in the field of health sciences.

          Methods

          Three online databases (Web of Science, Scopus, and PubMed) were systematically searched, without language restrictions, from the earliest publication date available through February 29, 2020, using the keywords “altmetric,” “citation,” and “correlation.” Grey literature was also searched via WorldCat, Open Grey, and Google Scholar (first 100 hits only). All studies in the field of health sciences that reported on this correlation were included. Effect sizes were calculated using Fisher's z transformation of correlations. Subgroup analyses based on citation source and sampling methods were performed.

          Results

          From 27 included articles, 8 articles comprise several independent studies. The total sample size was 9,943 articles comprised of 35 studies. The overall pooled effect size was 0.19 (95% confidence interval 0.13 to 0.26). Bivariate partial prediction of interaction between effect size, citation source, and sampling method showed a greater effect size with Web of Science compared with Scopus and Dimensions. Egger's regression showed a marginally nonsignificant publication bias ( p = 0.055), and trim-and-fill analysis estimated one missing study in this meta-analysis.

          Conclusion

          In health sciences, currently altmetric score has a positive but weak correlation with number of citations (pooled correlation = 0.19, 95% C.I 0.12 to 0.25). We emphasize on future examinations to assess changes of correlation pattern between altmetric score and citations over time.

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          Most cited references47

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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            There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
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              Machine learning: Trends, perspectives, and prospects.

              Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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                Author and article information

                Contributors
                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi
                2314-6133
                2314-6141
                2021
                7 April 2021
                : 2021
                : 6680764
                Affiliations
                1Independent Research Scientist, Founder of Dental Hypotheses, Isfahan, Iran
                2Department of Endodontics, School of Dentistry, Kermanshah University of Medical Sciences, Kermanshah, Iran
                3Department of Endodontics, Dental Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
                4Department of Orthopedic Surgery, Seoul Sacred Heart General Hospital, Seoul, Republic of Korea
                5Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, CA, USA
                Author notes

                Academic Editor: Paolo Boffano

                Author information
                https://orcid.org/0000-0002-2602-0443
                https://orcid.org/0000-0002-9085-1292
                https://orcid.org/0000-0002-4813-4097
                https://orcid.org/0000-0003-3380-1683
                https://orcid.org/0000-0002-5755-9951
                https://orcid.org/0000-0002-0810-7908
                Article
                10.1155/2021/6680764
                8046527
                33880377
                5e43405b-7598-48ba-acda-e9f20f2d2541
                Copyright © 2021 Jafar Kolahi et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 October 2020
                : 29 January 2021
                : 11 February 2021
                Funding
                Funded by: National Institutes of Health
                Award ID: ULI TR001860
                Categories
                Review Article

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