High-cited favorable studies for COVID-19 treatments ineffective in large trials

Importance. COVID-19 has resulted in massive production, publication and wide dissemination of clinical studies trying to identify effective treatments. However, several widely touted treatments failed to show effectiveness in large well-done randomized controlled trials (RCTs). Objective. To evaluate for COVID-19 treatments that showed no benefits in subsequent large RCTs how many of their most-cited clinical studies had declared favorable results for these interventions. Methods. Scopus (last update December 23, 2021) identified articles on lopinavir-ritonavir, hydroxycholoroquine/azithromycin, remdesivir, convalescent plasma, colchicine or interferon (index interventions) that represented clinical trials and that had received >150 citations. Their conclusions were assessed and correlated with study design features. The ten most recent citations for the most-cited article on each index intervention were examined on whether they were critical to the highly-cited study. Altmetric scores were also obtained. Findings. 40 articles of clinical studies on these index interventions had received >150 citations (7 exceeded 1,000 citations). 20/40 (50%) had favorable conclusions and 4 were equivocal. Highly-cited articles with favorable conclusions were rarely RCTs while those without favorable conclusions were mostly RCTs (3/20 vs 15/20, p=0.0003). Only 1 RCT with favorable conclusions had sample size >160. Citation counts correlated strongly with Altmetric scores, in particular news items. Only 9 (15%) of 60 recent citations to the most highly-cited studies with favorable or equivocal conclusions were critical to the highly-cited study. Conclusion. Many clinical studies with favorable conclusions for largely ineffective COVID-19 treatments are uncritically heavily cited and disseminated. Early observational studies and small randomized trials may cause spurious claims of effectiveness that get perpetuated.

Methodology of Writing Literature Review on Management and Business: Digest of Overseas Highly-Cited Articles

The goal of the research is to elaborate methodology how to systematize findings in the field of management published during previous years. Special attention is paid to typology of reviews. In this case the author distinguished systematic (meta-analysis as its variant), semi-systematic, integrative and other types of reviews. The article traces the origin of literature reviews starting from medical science to their use in articles concerning management in business. The author described difficulties of conducting meta-analysis and systematic analysis on materials of research on management and showed several methods of presenting results of systematic review of literature. Review articles can both rely on proofs obtained in previous quality (or mixed) methods of research and include quantitative data. Orientation to author is also possible. The most widely used type is a subject review, where researcher describes publications fostering the development of general comprehension of the concept or phenomenon being of interest, including for the future research. The choice of the review type and methodology of its conducting is determined by the data that should be analyzed. Authors should follow the chosen methodology.

Highly cited articles in social sciences: an analytical study

Purpose This paper aims at analyzing the distinctive characteristics of highly cited articles (HCAs) in the domain of Social Sciences with respect to chronological growth pattern, productive journals, authorship pattern, prolific authors, top institutions and leading countries, network among institutions and top ranked keywords in social science research. Design/methodology/approach The required data has been retrieved from Scopus indexing database and further refined using various limits like document types, subject coverage and total citations, and finally, 839 articles were selected for detail analysis. A set of bibliometric indicators were used to make a quantitative analysis, whereas VOSviewer software tool was used to visualize the institutional network and keywords mapping of the HCAs. Findings This study revealed that highest number of HCAs (371) were published during the decade 2001–2010. Degree of collaboration, collaborative index and collaborative coefficient were observed to be 0.513, 1.98 and 0.988, respectively. The highly cited papers were emanated from 397 journals, contributed by 1,556 authors from 1,326 institutions placed in 46 countries. Social Science and Medicine was the most productive journal; J. Urry of Lancaster University, UK, was the most influential author; the USA, the UK and Canada are the torchbearers in social science research. The paper entitled “Five misunderstandings about case-study research,” authored by B. Flyvbjerg, published in 2006 in Qualitative Inquiry, received highest 4,730 citations. Originality/value The primary value of this paper lies in extending an understanding of the characteristics of HCAs in the domain of social sciences. It will provide an insight to the researchers to get acquainted with the most influential authors, journals, institutions, countries and major thrust areas of research in social sciences.

Ecosystem Services Research Trends: A Bibliometric Analysis from 2000–2020

The study of ecosystem services (ES) is becoming increasingly popular, as it plays an important role in human wellbeing, economic growth, and livelihoods. The primary goal of this research is to investigate the global trend in ES research using a rigorous systematic review of highly cited articles. The articles for this study were extracted from Science Citation Index Expanded (SCI-E), Emerging Sources Citation Index (ESCI), and Social Sciences Citation Index (SSCI) databases of Web of Science Core Collection (WoSCC) covering the period from 2000 to 2020. This study was limited to SCI-E, ESCI, and SSCI databases of the Web of Science. The term “ecosystem service/s” has been used as a research term to filter the study sample and eliminate other databases from the analysis. A citation level equal to or greater than 200 was used to further filtration of articles. This query could restrict to 128 articles that are highly cited in the selected period. Bibliometric analysis results show that, according to the author’s keywords, the “ecosystem service/s” keyword is highly connected to the “biodiversity”, “valuation”, “marine spatial planning”, and “conservation planning”. The U.S.A., Canada, China, France, and Australia are the leading countries in the cumulative number of highly cited articles and networks of co-authors. The U.S.A. is a strong contributor to ES research with China, Canada, and France. The most productive universities linked to the United States were the University of Minnesota, the University of California-Santa Barbara (UC Santa Barbara), and the Chinese Academy of Science. The most significant and compelling author is Halpern S Benjamin, who represents UC Santa Barbara. He has earned international recognition for a model he developed to analyze global data sets of anthropogenic drivers of ecological change in marine environments. The most accessed and studied fields in the ES are terrestrial, urban, and marine environments.

Bibliometric Analysis of Telemedicine and E-Health Literature

Emerging technologies such as artificial intelligence, the internet of things, cloud computing, and wearable gadgets have broadened the scope of telemedicine and eHealth in past years. This research aims to investigate the past patterns of telemedicine and eHealth research over the past ten years. Scopus database was used to extract the article information of the selected articles. The research is conducted on 1401 articles in which we investigated the top authors, journals, institutions, countries, and highly cited articles. Our research findings project the growth trend of publications and the pattern of authors and distribution of articles and the core journals. The top 10 authors in the selected field were identified so were the highly cited articles. Co-occurrence analyses of keywords, authors, and countries co-authors analysis and bibliographic coupling of VOS viewer documents were conducted. The limitations and directions for future researchers have also been discussed.

Bibliometric Trends in Nail Psoriasis Research Publications

<b><i>Introduction:</i></b> Bibliometric analysis provides an objective assessment of current research patterns and highlights the impact of selected publications in any given scientific discipline. <b><i>Methods:</i></b> We sought to provide information about dynamic research trends in nail psoriasis by analyzing the 50 most cited articles on this topic, which were identified utilizing the Scopus citation database. <b><i>Results:</i></b> The median number of citations was 79 (range, 60–337) per article. Publication dates ranged from 1969 to 2020, while the majority of articles (46%) were published between 2000 and 2009. The top 50 highly cited articles were published in 19 different journals, with a median impact factor of 5.248 (range, 1.022–16.102). The <i>British Journal of Dermatology</i> published the greatest number of highly cited articles (<i>n</i> = 9). Most publications were original articles, and most cited research topics included medical treatment and correlation of nail psoriasis with psoriatic arthritis. Most publications originated from the USA and UK, while Phoebe Rich and Dennis McGonagle were the two most contributing authors. <b><i>Conclusion:</i></b> This analysis provides information about emerging bibliometric trends and may guide future research in the field of nail psoriasis.

A Content Analysis of the Highly-Cited Articles in Medical Education: Commitment to Addressing Change and Reliance On Theory

Background: The medical education community is reflecting increasingly on the role and nature of research in the field. Useful sources of data to include in these reflections are a description of the topics in which we are investing our energies, an analysis of the extent to which there is a sense of progress on these topics, and an examination of the mechanisms by which any progress has been achieved. The purpose of the present study was to provide an insight into the highly cited themes of research into medical education.Methods: This study is a content analysis of highly cited articles in medical education. An in-depth content analysis was conducted, identifying meaning units, which were compacted and coded with labels.Results: During a variety of topics, methods and strategies, 764 codes, 24 descriptive themes, and 7 categories were extracted from the content analysis as the most prominent. Categories for future medical education research were: Modern technology updating in medical education; Learner performance improving; Sociological aspects of medical education; Clinical reasoning; Research methodology concerns of medical education; Instructional design educational models; and Professional aspects of medical education.Conclusions: Medical education is in need of moving to a more theory and discovery driven approach and would profit from broadening its scope and reformation that might bring answers to new concerns. An emphasis on creating more systemic knowledge and theoretical models will nurture the generalizable scientific knowledge and will increase the medical education chance to drive the development of research on learning and instruction.

A Bayesian Hurdle Quantile Regression Model for Citation Analysis with Mass Points at Lower Values

Quantile regression presents a complete picture of the effects on the location, scale, and shape of the dependent variable at all points, not just the mean. We focus on two challenges for citation count analysis by quantile regression: discontinuity and substantial mass points at lower counts. A Bayesian hurdle quantile regression model for count data with a substantial mass point at zero was proposed by King and Song (2019). It uses quantile regression for modeling the nonzero data and logistic regression for modeling the probability of zeros versus nonzeros. We show that substantial mass points for low citation counts will nearly certainly also affect parameter estimation in the quantile regression part of the model, similar to a mass point at zero. We update the King and Song model by shifting the hurdle point past the main mass points. This model delivers more accurate quantile regression for moderately to highly cited articles, especially at quantiles corresponding to values just beyond the mass points, and enables estimates of the extent to which factors influence the chances that an article will be low cited. To illustrate the potential of this method, it is applied to simulated citation counts and data from Scopus. Peer Review https://publons.com/publon/10.1162/qss_a_00147