Pakistan Journal of Medical Sciences: A bibliometric assessment 2001-2010.
ABSTRACT: The aim of this study was to measure the growth of scientific research, authors' productivity, affiliation with the institute and geographic locations published in the Pakistan Journal of Medical Sciences during the period of 2001 - 2010.This numerical analysis was conducted during mid-August 2016 to mid-October, 2016. The data for the study was downloaded from websites of e-journal of Pakistan Journal of Medical Sciences (PJMS) and Pak Medi-Net Com.A total number of 1199 articled were covered by PJMS in 10 volumes and 40 issues with contribution of 3798 (3%) authors during 2001 - 2010. The average number of papers per issue is 30%. A gender wise contribution of males was higher 3050 (80%) than the females 748 (20%). A majority of articles were multi-authored 1052 (87%) as opposed to single author contribution 147 (13%). All 1199 articles were covered under four major disciplines i.e Basic medical sciences, medicine & allied, surgery & allied and radiological sciences and 39 sub-specialties according to medical subject headings (MeSH). It observed that 467 (39%) articles were published in Pakistan and 732 (61%) articles produced by other 32 countries. The Karachi city of Pakistan has produced 199 (16%) articles as highest as its national level and followed by Tehran (Iran) 77 (6%) as followed internationally.This study reveals that the participation of 32 countries in the PJMS publications proves it to be an internationally circulated journal to support research with the constant approach of publishing articles to each volume in basic medical sciences, biomedical, clinical and public health sciences. Abbreviations: DOAJ: Directory of Open Access Journals IMEMR: Index Medicus Eastern Mediterranean Region HEC: Higher Education Commission (Pakistan) PJMS: Pakistan Journal of Medical Sciences MeSH: Medical Subject Headings PMDC: Pakistan Medical & Dental Council SCIE: Science Citation Index Expanded.
Project description:<h4>Background</h4>Comparison of similarity and difference in research types among journals are concerned in literature. However, to date, none display the methodology seen in selecting similar journals related to the target journal, as similar articles did to a given article. Authors need 1 effective method not only to find similar journals for their studies but also to know the difference in methods. This study (1) shows the similar journals for the target journal online displayed, and (2) identifies the effect of similarity odds ratio compared to the counterparts using the forest plots in Meta-analysis and the major medical subject headings (MeSH terms).<h4>Methods</h4>We downloaded 1000 recent top 20 most similar articles related to the Respiratory Care journal from the PubMed library, plotted the clusters of related journals using social network analysis (SNA), and compared the MeSH terms in differences in an odds ratio unit using the forest plot relevant to Respiratory Care and the most similar journals. Q statistic and I-square (I2) index were used to evaluate the difference in the proportion of events.<h4>Results</h4>This study found that (1) the journals related to Respiratory Care are easily presented on Google Maps; (2) 10 journal clusters were identified using SNA; (3) the top 3 MeSH terms are methods, therapy, and physiopathology, and (4) the odds ratios of MeSH terms between journals associated with the Respiratory Care showing different from Int J Chron Obstruct Pulmori Dis and similar to Curr Opin Endocrinol Diabetes Obes within heterogeneity with I2 = 70.5% (P?<?0.001) and 0% (P = 0.803), respectively.<h4>Conclusions</h4>SNA and forest plots provide deep insight into the relationships between journals in MeSH terms. The results of this research can provide readers with a concept diagram that can be used for future submissions to a given journal.
Project description:For the biomedical sciences, the Medical Subject Headings (MeSH) make available a rich feature which cannot currently be merged properly with widely used citing/cited data. Here, we provide methods and routines that make MeSH terms amenable to broader usage in the study of science indicators: using Web-of-Science (WoS) data, one can generate the matrix of citing versus cited documents; using PubMed/MEDLINE data, a matrix of the citing documents versus MeSH terms can be generated analogously. The two matrices can also be reorganized into a 2-mode matrix of MeSH terms versus cited references. Using the abbreviated journal names in the references, one can, for example, address the question whether MeSH terms can be used as an alternative to WoS Subject Categories for the purpose of normalizing citation data. We explore the applicability of the routines in the case of a research program about the amyloid cascade hypothesis in Alzheimer's disease. One conclusion is that referenced journals provide archival structures, whereas MeSH terms indicate mainly variation (including novelty) at the research front. Furthermore, we explore the option of using the citing/cited matrix for main-path analysis as a by-product of the software.
Project description:<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>We found that Kendall W is 0.97 (? = 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.<h4>Conclusion</h4>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.
Project description:MOTIVATION:With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as Medical Text Indexer, MeSHLabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is hampered by using limited information, i.e. only the title and abstract of biomedical articles. RESULTS:We propose FullMeSH, a large-scale MeSH indexing method taking advantage of the recent increase in the availability of full text articles. Compared to DeepMeSH and other state-of-the-art methods, FullMeSH has three novelties: (i) Instead of using a full text as a whole, FullMeSH segments it into several sections with their normalized titles in order to distinguish their contributions to the overall performance. (ii) FullMeSH integrates the evidence from different sections in a 'learning to rank' framework by combining the sparse and deep semantic representations. (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which achieves better performance on infrequent MeSH headings. FullMeSH has been developed and empirically trained on the entire set of 1.4 million full-text articles in the PubMed Central Open Access subset. It achieved a Micro F-measure of 66.76% on a test set of 10 000 articles, which was 3.3% and 6.4% higher than DeepMeSH and MeSHLabeler, respectively. Furthermore, FullMeSH demonstrated an average improvement of 4.7% over DeepMeSH for indexing Check Tags, a set of most frequently indexed MeSH headings. AVAILABILITY AND IMPLEMENTATION:The software is available upon request. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Project description:Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.
Project description:<h4>Background</h4>Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children or early adolescents with an estimated worldwide prevalence of 7.2%. Numerous articles related to ADHD have been published in the literature. However, which articles had ultimate influence is still unknown, and what factors affect the number of article citations remains unclear as well. This bibliometric analysis (1) visualizes the prominent entities with 1 picture using the top 100 most-cited articles, and (2) investigates whether medical subject headings (i.e., MeSH terms) can be used in predicting article citations.<h4>Methods</h4>By searching the PubMed Central<sup>®</sup> (PMC) database, the top 100 most-cited abstracts relevant to ADHD since 2014 were downloaded. Citation rank analysis was performed to compare the dominant roles of article types and topic categories using the pyramid plot. Social network analysis (SNA) was performed to highlight prominent entities for providing a quick look at the study result. The authors examined the MeSH prediction effect on article citations using its correlation coefficients (CC).<h4>Results</h4>The most frequent article types and topic categories were research support by institutes (56%) and epidemiology (28%). The most productive countries were the United States (42%), followed by the United Kingdom (13%), Germany (9%), and the Netherlands (9%). Most articles were published in the Journal of the American Academy of Child and Adolescent Psychiatry (15%) and JAMA Psychiatry (9%). MeSH terms were evident in prediction power on the number of article citations (correlation coefficient?=?0.39; t?=?4.1; n?=?94; 6 articles were excluded because they do not have MeSH terms).<h4>Conclusions</h4>The breakthrough was made by developing 1 dashboard to display 100 top-cited articles on ADHD. MeSH terms can be used in predicting article citations on ADHD. These visualizations of the top 100 most-cited articles could be applied to future academic pursuits and other academic disciplines.
Project description:Within well-established fields of biomedical science, we identify "gaps", topical areas of investigation that might be expected to occur but are missing. We define a field by carrying out a topical PubMed query, and analyze Medical Subject Headings by which the set of retrieved articles are indexed. Medical Subject headings (MeSH terms) which occur in >1% of the articles are examined pairwise to see how often they are predicted to co-occur within individual articles (assuming that they are independent of each other). A pair of MeSH terms that are predicted to co-occur in at least 10 articles, yet are not observed to co-occur in any article, are "gaps" and were studied further in a corpus of 10 disease-related article sets and 10 related to biological processes. Overall, articles that filled gaps were cited more heavily than non-gap-filling articles and were 61% more likely to be published in multidisciplinary high-impact journals. Nine different features of these "gaps" were characterized and tested to learn which, if any, correlate with the appearance of one or more articles containing both MeSH terms within the next five years. Several different types of gaps were identified, each having distinct combinations of predictive features: a) those arising as a byproduct of MeSH indexing rules; b) those having little biological meaning; c) those representing "low hanging fruit" for immediate exploitation; and d) those representing gaps across disciplines or sub-disciplines that do not talk to each other or work together. We have built a free, open tool called "Mine the Gap!" that identifies and characterizes the "gaps" for any PubMed query, which can be accessed via the Anne O'Tate value-added PubMed search interface (http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi).
Project description:High-throughput sequencing technology, also called next-generation sequencing (NGS), has the potential to revolutionize the whole process of genome sequencing, transcriptomics, and epigenetics. Sequencing data is captured in a public primary data archive, the Sequence Read Archive (SRA). As of January 2013, data from more than 14,000 projects have been submitted to SRA, which is double that of the previous year. Researchers can download raw sequence data from SRA website to perform further analyses and to compare with their own data. However, it is extremely difficult to search entries and download raw sequences of interests with SRA because the data structure is complicated, and experimental conditions along with raw sequences are partly described in natural language. Additionally, some sequences are of inconsistent quality because anyone can submit sequencing data to SRA with no quality check. Therefore, as a criterion of data quality, we focused on SRA entries that were cited in journal articles. We extracted SRA IDs and PubMed IDs (PMIDs) from SRA and full-text versions of journal articles and retrieved 2748 SRA ID-PMID pairs. We constructed a publication list referring to SRA entries. Since, one of the main themes of -omics analyses is clarification of disease mechanisms, we also characterized SRA entries by disease keywords, according to the Medical Subject Headings (MeSH) extracted from articles assigned to each SRA entry. We obtained 989 SRA ID-MeSH disease term pairs, and constructed a disease list referring to SRA data. We previously developed feature profiles of diseases in a system called "Gendoo". We generated hyperlinks between diseases extracted from SRA and the feature profiles of it. The developed project, publication and disease lists resulting from this study are available at our web service, called "DBCLS SRA" (http://sra.dbcls.jp/). This service will improve accessibility to high-quality data from SRA.
Project description:<h4>Background</h4>Citation analysis has been increasingly applied to assess the quantity and quality of scientific research in various fields worldwide. However, these analyses on spinal surgery do not provide visualization of results. This study aims (1) to evaluate the worldwide research citations and publications on spinal surgery and (2) to provide visual representations using Kano diagrams onto the research analysis for spinal surgeons and researchers.<h4>Methods</h4>Article abstracts published between 2007 and 2018 were downloaded from PubMed Central (PMC) in 5 journals, including Spine, European Spine Journal, The Spine Journal, Journal of Neurosurgery: Spine, and Journal of Spinal Disorders and Techniques. The article types, affiliated countries, authors, and Medical subject headings (MeSH terms) were analyzed by the number of article citations using x-index. Choropleth maps and Kano diagrams were applied to present these results. The trends of MeSH terms over the years were plotted and analyzed.<h4>Results</h4>A total of 18,808 publications were extracted from the PMC database, and 17,245 were affiliated to countries/areas. The 12-year impact factor for the five spine journals is 5.758. We observed that (1) the largest number of articles on spinal surgery was from North America (6417, 37.21%). Spine earns the highest x-index (=?82.96). Comparative Study has the highest x-index (=?66.74) among all article types. (2) The United States performed exceptionally in x-indexes (=?56.86 and 44.5) on both analyses done on the total 18,808 and the top 100 most cited articles, respectively. The most influential author whose x-index reaches 15.11 was Simon Dagenais from the US. (3) The most cited MeSH term with an x-index of 23.05 was surgery based on the top 100 most cited articles. The most cited article (PMID?=?18164449) was written by Dagenais and his colleagues in 2008. The most productive author was Michael G. Fehlings, whose x-index and the author's impact factor are 13.57(=??(13.16*14)) and 9.86(=?331.57/33.64), respectively.<h4>Conclusions</h4>There was a rapidly increasing scientific productivity in the field of spinal surgery in the past 12 years. The US has extraordinary contributions to the publications. Furthermore, China and Japan have increasing numbers of publications on spinal surgery. This study with Kano diagrams provides an insight into the research for spinal surgeons and researchers.
Project description:<h4>Background</h4>The influence of social media among adolescent peer groups can be a powerful change agent.<h4>Objective</h4>Our scoping review aimed to elucidate the ways in which social media use among adolescent peers influences eating behaviors.<h4>Methods</h4>A scoping review of the literature of articles published from journal inception to 2019 was performed by searching PubMed (ie, MEDLINE), Embase, CINAHL, PsycINFO, Web of Science, and other databases. The review was conducted in three steps: (1) identification of the research question and clarification of criteria using the population, intervention, comparison, and outcome (PICO) framework; (2) selection of articles from the literature using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines; and (3) charting and summarizing information from selected articles. PubMed's Medical Subject Headings (MeSH) and Embase's Emtree subject headings were reviewed along with specific keywords to construct a comprehensive search strategy. Subject headings and keywords were based on adolescent age groups, social media platforms, and eating behaviors. After screening 1387 peer-reviewed articles, 37 articles were assessed for eligibility. Participant age, gender, study location, social media channels utilized, user volume, and content themes related to findings were extracted from the articles.<h4>Results</h4>Six articles met the final inclusion criteria. A final sample size of 1225 adolescents (aged 10 to 19 years) from the United States, the United Kingdom, Sweden, Norway, Denmark, Portugal, Brazil, and Australia were included in controlled and qualitative studies. Instagram and Facebook were among the most popular social media platforms that influenced healthful eating behaviors (ie, fruit and vegetable intake) as well as unhealthful eating behaviors related to fast food advertising. Online forums served as accessible channels for eating disorder relapse prevention among youth. Social media influence converged around four central themes: (1) visual appeal, (2) content dissemination, (3) socialized digital connections, and (4) adolescent marketer influencers.<h4>Conclusions</h4>Adolescent peer influence in social media environments spans the spectrum of healthy eating (ie, pathological) to eating disorders (ie, nonpathological). Strategic network-driven approaches should be considered for engaging adolescents in the promotion of positive dietary behaviors.