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10.1016/j.dsx.2021.05.013

http://scihub22266oqcxt.onion/10.1016/j.dsx.2021.05.013
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34186371!8744451!34186371
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suck abstract from ncbi


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pmid34186371      Diabetes+Metab+Syndr 2021 ; 15 (4): 102140
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  • Top 50 cited articles on Covid-19 after the first year of the pandemic: A bibliometric analysis #MMPMID34186371
  • Kambhampati SBS; Vasudeva N; Vaishya R; Patralekh MK
  • Diabetes Metab Syndr 2021[Jul]; 15 (4): 102140 PMID34186371show ga
  • BACKGROUND & AIMS: It has been just over a year since the Covid-19 pandemic started. The top 50 cited articles on this subject would help identify trends and focus on the research efforts. METHODS: We utilised e-utilities in PubMed to find publications on Covid-19 until the date of search on 7/2/21. The iCite website was used to find the top 50 citations of the output from the search strategy. We looked into their full text for the editorial dates, type of study, level of evidence, focus of the article and country of origin. We also counted the errata and comments on each of them. RESULTS: The total number of citations of all 50 articles was 123,960, the highest being 10, 754 for a single article. Huang C was the most cited first author. They were published from week 4-17, with February being the month with most citations. Lancet was the most cited journal, having published 9 of the 50 articles. Majority belonged to level 3 of the evidence ladder and were retrospective studies. Thirty percent of them had an errata published and an average of 7 comments per article. CONCLUSION: The top 50 most cited articles identify the most impactful studies on Covid-19, providing a resource to educators while identifying trends to guide research and publishing efforts. There has been an explosion of publications and an unprecedented rate and number of citations within the first year for any single condition in the literature.
  • |*Bibliometrics[MESH]
  • |COVID-19/diagnosis/*epidemiology/therapy/virology[MESH]
  • |Humans[MESH]
  • |Journal Impact Factor[MESH]
  • |Publications/*statistics & numerical data[MESH]


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