Influencia de los distintos tipos de artículo en el factor de impacto de las revistas dermatológicas

Bibliometric Study of the Advances Made in Clinical Dermatological Research in Spain from 2015 through 2021: Update of the MaIND Project and Interactive Online Map

INTRODUCTION

A clinical dermatological research was conducted in Spain from 2005 through 2014 as part of the MaIND project with the provinces or centers with the highest number of published articles. However, a low level of evidence in scientific production was confirmed as the overall result. The aim of this study is to update the Spanish clinical dermatological research in bibliometric terms from 2015 through 2021 with comparisons between both periods of time.

MATERIAL AND METHODS

We conducted a bibliometric study to replicate the methodology used in the article to be updated. We included articles whose corresponding authors' affiliation was a Spanish dermatological center, which met the criteria for clinical research in dermatology, including a level of evidence ≤4.

RESULTS

A total of 1,674 out of the 10,199 articles met the inclusion criteria. An interactive map representing quantitative and qualitative indicators calculated for the 2005-2021 is presented here. In the study period, we found an increasing trend both in the number of published articles (P<.002) and in the mean number of citation-years per article (P<.01). A total of 22 of the articles had a level of evidence >4, with a positive trend towards more articles having a higher level of evidence (P<.03). ACTAS DERMOSIFILOGRÁFICAS still maintains its position as the journal with the highest number of articles received (18%, a total of 302 articles).

CONCLUSIONS

The results of this study show that, in Spain, the scientific production of dermatology represents an upward trend in quantity, impact, and level of evidence.

The impact of COVID-19 on global health journals: an analysis of impact factor and publication trends

BACKGROUND

COVID-19 has affected research productivity across all areas of knowledge. Current evidence suggests that COVID-19 has had a blockbuster effect on journal impact factors (JIFs) and publication trends, while little is known on global health journals.

METHODS

Twenty global health journals were included to analyse the impact of COVID-19 on their JIFs and publication trends. Indicator data, including numbers of publications, citations, articles with different types, etc, were extracted from journal websites and Web of Science Core Collection database. The JIFs from 2019 to 2021 were simulated for longitudinal and cross-sectional analyses. Interrupted time-series analysis and non-parametric tests were applied to assess whether COVID-19 had decreased non-COVID-19 publications from January 2018 to June 2022.

RESULTS

In 2020, 615 out of 3223 publications were COVID-19 related, accounting for 19.08%. The simulated JIFs of 17 out of 20 journals in 2021 were higher than those in 2019 and 2020. Notably, 18 out of 20 journals had a decrease in their simulated JIFs after excluding COVID-19-related publications. Moreover, 10 out of 20 journals decreased their monthly numbers of non-COVID-19 publications after the COVID-19 outbreak. For all the 20 journals as a whole, after the COVID-19 outbreak in February 2020, the total number of non-COVID-19 publications significantly decreased by 14.2 compared with the previous month (p=0.013), and since then, on average, the publications had decreased by 0.6 per month until June 2022 (p<0.001).

CONCLUSIONS

COVID-19 has impacted the structure of COVID-19-related publications, the JIFs of global health journals and their numbers of non-COVID-19 publications. Although journals may benefit from increased JIFs, global health journals should avoid relying on a single metric. More follow-up studies including more years of data with a combination of metrics should be conducted to generate more robust evidence.

The 100 most cited articles have fewer citations than other bibliometric articles: A pairwise comparison using a temporal bubble graph

BACKGROUND

More than 400 articles with the title of 100 top-cited articles (Top100) have been published in PubMed. It is unknown whether their citations are fewer (or more) than those found in other bibliometric studies (Nontop100). After determining article themes using coword analysis, a temporal bubble graph (TBG) was used to verify the hypothesis that the Top100 had fewer citations than the Nontop100.

METHODS

Using the Web of Science core collection, the top 50 most cited articles were compiled by Top100 and Nontop100, respectively, based on the research area of biomedicine and bibliometrics only. Coword analysis was used to extract themes. The study results were displayed using 6 different visualizations, including charts with bars, pyramids, forests, clusters, chords, and bubbles. Mean citations were compared between Top100 and Nontop100 using the bootstrapping method.

RESULTS

There were 18 citations in total for the 2 sets of the 50 most cited articles (range 1-134; 5 and 26.5 for Top100 and Nontop100, respectively). A significant difference in mean citations was observed between the 2 groups of Top100 and Nontop100 based on the bootstrapping method (3, 95% confidence interval: [1.18, 4.82]; 26.5, 95% confidence interval: [23.82, 29.18], P < .001). The 11 themes were clustered using coword analysis and applied to a TBG, which is composed of 4 dimensions: themes, years, citations and groups of articles. Among the 2 groups, the majority of articles were published in the journal of Medicine (Baltimore), with 9 and 7, respectively.

CONCLUSION

Eleven themes were identified as a result of this study. In addition, it reveals distinct differences between the 2 groups of Top100 and Nontop100, with the former containing more recently published articles and the latter containing more citations for articles. Clinical and research clinicians and researchers can use bibliometric analysis to appraise published literature and to understand the scientific landmark using TBG in bibliometrics.

Impact factor JUMPS after the 2020 COVID-19 pandemic: a retrospective study in Dermatology journals

Background

The term “JUMPS” was used to describe the impact factor (IF) in an article published in PubMed in 2021, representing an increase of more than 40% of IF.

Aims

In this study, we aimed to compare the growth rate of IF JUMPS in Dermatology in the last 5 years, and particularly the effect of the 2020 COVID-19 pandemic.

Methods

This study evaluated the growth rate (JUMP) in IF from 2016 to 2020. We used the Friedman and Wilcoxon signed ranks tests. We classified JUMPS in negative growth rate; Q1 to Q4 quartiles; and journals with > 100%. A 76–100% growth rate was observed in five (7%) journals, and twelve journals (17%) depicted a 51–75% percentage of change.

Results

Several journals in the Dermatology category increased their IF by 50%. Repeated measures analyses showed a significant difference (p < .001).

Conclusion

Although we found journals with growth rates in the four quartiles, no journals depicted negative growth rates nor > 100% growth. Knowing the growing trends in this category might supplement the assessment of target journals for authors looking to submit their works.

Classification and citation analysis of the 100 top-cited articles on adult spinal deformity since 2011: A bibliometric analysis

BACKGROUND

Studies of the 100 most-cited articles are reported for many subjects. However, none has analyzed the article characteristics associated with high citation frequency. This study aims to (1) graphically depict characteristics of the 100 top-cited articles addressing adult spinal deformity (ASD), (2) diagram the association between articles according to subject and major topic medical subject headings (MeSHs), and (3) investigate whether major topic MeSH correlates with article citation frequency.

METHODS

The 100 top-cited ASD publications since 2011 were retrieved using a PubMed Central search on May 6, 2020. Using titles and abstracts, eight subject categories were identified: surgery, conservative treatment, normal values in spinopelvic alignment, review, cervical alignment, classification, compensatory mechanism, and spine-hip relationship. Sankey diagrams were used to organize the information. Network analysis was performed according to article subject and major topic MeSHs. Pearson's r was used to determine whether the weighted number of citations correlates with major topic MeSHs and the number of citations.

RESULTS

The average number of citations per article was 34.8 (range, 19-156). The most represented country was USA (n = 51). The most productive and highly cited journal was Spine (Phila Pa 1976) (n = 34; average, 38.2 citations per article). The most frequent subject categories and major topic MeSHs were "surgery" (n = 53) and "scoliosis" (weighted count, 9.8), while articles with the subject "compensatory" had the highest average number of citations (64.7). The most highly cited article, by Dr. F. Schwab in 2012, had 156 citations. Network analysis revealed the relationships between these articles according to major topic MeSHs. The weighted number of citations according to major topic MeSHs correlated significantly with article citation frequency (Pearson's r, 0.57; p < 0.001).

CONCLUSION

Multiple characteristics of the 100 top-cited ASD articles are presented in diagrams to guide evidence-based clinical decision-making in ASD.

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis

BACKGROUND

Publications regarding the 100 top-cited articles in a given discipline are common, but studies reporting the association between article topics and their citations are lacking. Whether or not reviews and original articles have a higher impact factor than case reports is a point for verification in this study. In addition, article topics that can be used for predicting citations have not been analyzed. Thus, this study aims to METHODS:: We searched PubMed Central and downloaded 100 top-cited abstracts in the journal Medicine (Baltimore) since 2011. Four article types and 7 topic categories (denoted by MeSH terms) were extracted from abstracts. Contributors to these 100 top-cited articles were analyzed. Social network analysis and Sankey diagram analysis were performed to identify influential article types and topic categories. MeSH terms were applied to predict the number of article citations. We then examined the prediction power with the correlation coefficients between MeSH weights and article citations.

RESULTS

The citation counts for the 100 articles ranged from 24 to 127, with an average of 39.1 citations. The most frequent article types were journal articles (82%) and comparative studies (10%), and the most frequent topics were epidemiology (48%) and blood and immunology (36%). The most productive countries were the United States (24%) and China (23%). The most cited article (PDID = 27258521) with a count of 135 was written by Dr Shang from Shandong Provincial Hospital Affiliated to Shandong University (China) in 2016. MeSH terms were evident in the prediction power of the number of article citations (correlation coefficients  = 0.49, t = 5.62).

CONCLUSION

The breakthrough was made by developing dashboards showing the overall concept of the 100 top-cited articles using the Sankey diagram. MeSH terms can be used for predicting article citations. Analyzing the 100 top-cited articles could help future academic pursuits and applications in other academic disciplines.

Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis

BACKGROUND

Team science research includes authors from various fields collaborating to publish their work on certain topics. Despite the numerous papers that discussed the ordering of author names and the contributions of authors to an article, no paper evaluatedIn addition, few researchers publish academic articles without co-author collaboration. Whether the bibliometric indexes (eg, h-/x-index) of sole-author researchers are higher than those of other types of multiple authors is required for comparison. We aimed to evaluate a productive author who published 114 sole-author articles with exceptional RA and RD in academics.

METHODS

By searching the PubMed database (Pubmed.com), we used the keyword of (Taiwan[affiliation]) from 2016 to 2017 and downloaded 29,356 articles. One physician (Dr. Tseng from the field of Internal Medicine) who published 12 articles as a single author was selected. His articles and citations were searched in PubMed. A comparison of various types of author ordering placements was conducted using sensitivity analysis to inspect whether this sole author earns the highest metrics in RA. Social network analysis (SNA), Gini coefficient (GC), pyramid plot, and the Kano diagram were applied to gather the following data for visualization: RESULTS:: We observed that CONCLUSIONS:: The metrics on RA are high for the sole author studied. The author's RD can be denoted by the MeSH terms and measured by the GC. The author-weighted scheme is required for quantifying author credits in an article to evaluate the author's RA. Social network analysis incorporating the Kano diagrams provided insights into the relationships between actors (eg, coauthors, MeSH terms, or journals). The methods used in this study can be replicated to evaluate other productive studies on RA and RD in the future.

The Most-Cited Authors Who Published Papers in JMIR mHealth and uHealth Using the Authorship-Weighted Scheme: Bibliometric Analysis

Background

Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors’ contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network’s characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison.

Objective

This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research.

Methods

We obtained 676 abstracts from Medline based on the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied.

Results

We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps.

Conclusions

The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.

Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research

In order to improve individual research achievements (IRA), this study investigates which affiliated countries and authors earn the most cited IRAs and whether those types of articles are associated with the number of cited papers on schizophrenia from a leading journal in the field. The Kano model was used for displaying the IRAs. Clusters of medical subject headings (MeSH) were applied to explore the core concepts of a given journal. This study aimed to apply social network analysis (SNA) and an authorship-weighted scheme (AWS) to inspect the association between MeSH terms and IRA. About 2,008 abstracts published between 2012 and 2016 in the journal Schizophrenia Research were downloaded from Pubmed Central using the keyword (Schizophr Res)[Journal] on September 20, 2018. The MeSH terms were clustered by using SNA to separate the core concepts and compare the differences in bibliometric indices (i.e., h, Ag, x and author impact factor or AIF). Visual dashboards were shown on Google Maps. Results indicate that (1) the US, the UK, and Canada earn the highest x-index; (2) the top one author from the US has the highest x-index (= 5.73 with x-core at cited = 16.44 and citable = 2); (3) the article type of schizophrenic psychology shows distinctly higher frequencies than others; and (4) article types are associated with the number of cited papers. Four approaches of the Kano model, SNA, MeSH terms, and AWS can be accommodated to display IRAs, classify article types, and quantify coauthor contributions in the article byline, respectively, and applied to other scientific disciplines in the future, not just in this specific journal.

Whether article types of a scholarly journal are different in cited metrics using cluster analysis of MeSH terms to display: A bibliometric analysis

BACKGROUND

Many authors are concerned which types of peer-review articles can be cited most in academics and who were the highest-cited authors in a scientific discipline. The prerequisites are determined by: (1) classifying article types; and (2) quantifying co-author contributions. We aimed to apply Medical Subject Headings (MeSH) with social network analysis (SNA) and an authorship-weighted scheme (AWS) to meet the prerequisites above and then demonstrate the applications for scholars.

METHODS

By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 5,636 articles published from 2012 to 2016. A total number of 9,758 were cited in Pubmed Central (PMC). Ten MeSH terms were separated to represent the journal types of clusters using SNA to compare the difference in bibliometric indices, that is, h, g, and x as well as author impact factor(AIF). The methods of Kendall coefficient of concordance (W) and one-way ANOVA were performed to verify the internal consistency of indices and the difference across MeSH clusters. Visual representations with dashboards were shown on Google Maps.

RESULTS

We found that Kendall W is 0.97 (χ = 26.22, df = 9, P < .001) congruent with internal consistency on metrics across MeSH clusters. Both article types of methods and therapeutic use show higher frequencies than other 8 counterparts. The author Klaus Lechner (Austria) earns the highest research achievement(the mean of core articles on g = Ag = 15.35, AIF = 21, x = 3.92, h = 1) with one paper (PMID: 22732949, 2012), which was cited 23 times in 2017 and the preceding 5 years.

CONCLUSION

Publishing article type with study methodology and design might lead to a higher IF. Both classifying article types and quantifying co-author contributions can be accommodated to other scientific disciplines. As such, which type of articles and who contributes most to a specific journal can be evaluated in the future.

The Curious Case of "Case Report" of Infections Caused by Human and Animal Fungal Pathogens: An Educational Tool, an Online Archive, or a Format in Need of Retooling

Case reports describe the unusual occurrence and complications of diseases, diagnostic challenges, and notable therapeutic successes. Some journals have discontinued the case reports, while new case report journals have appeared in recent years. During the eightieth anniversary of Mycopathologia, it is fitting to examine the relevance of the case report since the journal continues to traverse the boundaries of basic and clinical sciences. A random sample of recent case reports and other articles were selected from Mycopathologia. Springer Nature individual article download statistics, and Google Scholar and Scopus citations numbers were compared to assess the reader access and bibliometric impact of case reports. Our analysis indicated that the case report format continues to be a vital element of publication in a cross-disciplinary journal such as Mycopathologia. Medical and veterinary case reports covering fungal pathogens are widely read as evident from their download numbers. The download numbers have a positive correlation with the completeness of the report, the topics and geographic origin of reports have a neutral influence, and the recency leads to lower downloads. There is no discernible trend between the download numbers and the citations of case reports as measured by Google Scholar and Scopus. A specially designed checklist for Mycopathologia case reports and new format MycopathologiaIMAGES are being introduced to improve the quality and relevance of case reports further.