Highly cited papers in rheumatology: identification and conceptual analysis

Pérez-Cabezas, Verónica; Carmona-Barrientos, Ines; Herrera‐Viedma, Enrique; Cobo, Manuel J.; Moral-Muñoz, José A.

doi:10.1007/s11192-018-2712-z

Cited by 20 publications

(14 citation statements)

References 41 publications

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“…The varying quality and quantity of the conducted research overtime can hamper a detailed analysis of any scientific field. While an expert in the field could develop a detailed analysis, it would nevertheless be a daunting and tedious task to undertake (Perez-Cabezas et al, 2018). For this reason, the use of scientific support tools, such as bibliometrics, can be used to facilitate the analysis of a research field by automatically classifying studies overtime into different themes and topics (Cobo et al, 2011b;Gutiérrez-Salcedo et al, 2017;Van Raan, 2005).…”

Section: Introductionmentioning

confidence: 99%

Thematic Trends in Complementary and Alternative Medicine Applied in Cancer-Related Symptoms

Moral-Muñoz

Arroyo-Morales

Piper

et al. 2018

Journal of Data and Information Science

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Purpose:The main goal of this study is to discover the scientific evolution of Cancer-Related Symptoms in Complementary and Alternative Medicine research area, analyzing the articles indexed in the Web of Science database from 1980 to 2013.Design/Methodology/Approach: A co-word science mapping analysis is performed under a longitudinal framework (1980 to 2013). The documental corpus is divided into two subperiods, 1980-2008 and 2009-2013. Thus, the performance and impact rates, and conceptual evolution of the research field are shown.Findings: According to the results, the co-word analysis allows us to identify 12 main thematic areas in this emerging research field: anxiety, survivors and palliative care, meditation, treatment, symptoms and cancer types, postmenopause, cancer pain, low back pain, herbal medicine, children, depression and insomnia, inflammation mediators, and lymphedema. The different research lines are identified according to the main thematic areas, centered fundamentally on anxiety and suffering prevention. The scientific community can use this information to identify where the interest is focused and make decisions in different ways.Research limitation: Several limitations can be addressed: 1) some of the Complementary and Alternative Medicine therapies may not have been included; 2) only the documents indexed in Web of Science are analyzed; and 3) the thematic areas detected could change if another dataset was considered. Practical implications:The results obtained in the present study could be considered as an evidence-based framework in which future studies could be built.Originality/value: Currently, there are no studies that show the thematic evolution of this research area.

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Section: Introductionmentioning

confidence: 99%

Thematic Trends in Complementary and Alternative Medicine Applied in Cancer-Related Symptoms

Moral-Muñoz

Arroyo-Morales

Piper

et al. 2018

Journal of Data and Information Science

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“…Interestingly, only 25% of the USA HCPs were categorised as multiple country publications (MCP), the lowest ratio among the 12 top countries measured, after Canada and Germany. To relate gross domestic product (GDP) with the production of HCPs, Table 5 also displays the ranking of countries ordered by Adjustment Index (AI) based on GDP per capita [ 28 ]. The adjustment index (AI) was calculated as AI = ((total number of HCP/GDP per capita of the country) × 100).…”

Section: Resultsmentioning

confidence: 99%

“…Two great advantages of using H-Classics is that it provides standardization of the criteria and thresholds used for selecting HCPs and includes the collection of papers published in a given field and their impact on a single procedure [ 26 ]. The H-Classics methodology has already been applied to medical areas such as paediatrics [ 27 ], dentistry [ 18 ], rheumatology [ 28 ], and microbiology [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Highly Cited Papers in Mass Cytometry through H-Classics

Zeo-Sánchez

Sánchez-Núñez

Stephens

et al. 2021

Biology

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Mass cytometry (CyTOF) is a relatively novel technique for the multiparametric analysis of single-cell features with an increasing central role in cell biology, immunology, pharmacology, and biomedicine. This technique mixes the fundamentals of flow cytometry with mass spectrometry and is mainly used for in-depth studies of the immune system and diseases with a significant immune load, such as cancer, autoimmune diseases, and viral diseases like HIV or the recently emerged COVID-19, produced by the SARS-CoV-2 coronavirus. The objective of this study was to provide a useful insight into the evolution of the mass cytometry research field, revealing the knowledge structure (conceptual and social) and authors, countries, sources, documents, and organizations that have made the most significant contribution to its development. We retrieved 937 articles from the Web of Science (2010–2019), analysed 71 Highly Cited Papers (HCP) through the H-Classics methodology and computed the data by using Bibliometrix R package. HCP sources corresponded to high-impact journals, such as Nature Biotechnology and Cell, and its production was concentrated in the US, and specifically Stanford University, affiliation of the most relevant authors in the field. HCPs analysis confirmed great interest in the study of the immune system and complex data processing in the mass cytometry research field.

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“…Bornmann and Marx (2014) gave information about a researcher's productivity and the impact of their publications based on 10% percentiles of citations, who worked in the natural and life sciences. Moral-Munoz et al (2018) and Perez-Cabezas et al (2018) identified and conceptualized microbiology and rheumatology by highly cited papers. Ye et al (2013) provided new insight into the relationship between Nobel awards and landmark papers in physiology or medicine.…”

Section: Journal Of Data and Information Sciencementioning

confidence: 99%

Identifying Scientific and Technical “Unicorns”

2020

Journal of Data and Information Science

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PurposeUsing the metaphor of “unicorn,” we identify the scientific papers and technical patents characterized by the informetric feature of very high citations in the first ten years after publishing, which may provide a new pattern to understand very high impact works in science and technology.Design/methodology/approachWhen we set CT as the total citations of papers or patents in the first ten years after publication, with CT≥ 5,000 for scientific “unicorn” and CT≥ 500 for technical “unicorn,” we have an absolute standard for identifying scientific and technical “unicorn” publications.FindingsWe identify 165 scientific “unicorns” in 14,301,875 WoS papers and 224 technical “unicorns” in 13,728,950 DII patents during 2001–2012. About 50% of “unicorns” belong to biomedicine, in which selected cases are individually discussed. The rare “unicorns” increase following linear model, the fitting data show 95% confidence with the RMSE of scientific “unicorn” is 0.2127 while the RMSE of technical “unicorn” is 0.0923.Research limitationsA “unicorn” is a pure quantitative consideration without concerning its quality, and “potential unicorns” as CT≤5,000 for papers and CT≤500 for patents are left in future studies.Practical implicationsScientific and technical “unicorns” provide a new pattern to understand high-impact works in science and technology. The “unicorn” pattern supplies a concise approach to identify very high-impact scientific papers and technical patents.Originality/valueThe “unicorn” pattern supplies a concise approach to identify very high impact scientific papers and technical patents.

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Highly cited papers in rheumatology: identification and conceptual analysis

Cited by 20 publications

References 41 publications

Thematic Trends in Complementary and Alternative Medicine Applied in Cancer-Related Symptoms

Thematic Trends in Complementary and Alternative Medicine Applied in Cancer-Related Symptoms

Characterizing Highly Cited Papers in Mass Cytometry through H-Classics

Identifying Scientific and Technical “Unicorns”

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