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A well-written and interesting article was published on November 21, 2021. Future relevant studies, however, may be improved by implementing (1) a framework that outlines the overall research; (2) an author-weighted scheme (AWS) that accurately quantifies the contributions of entities to articles; and (3) a more appropriate size for the nodes representing the proportional counts for each entity in social network analysis (SNA). VOSviewer was used to construct and visualize the scientometric networks and the relation-based analyses included three categories: (1) citation relations, (2) word cooccurrences, and (3) coauthorship relations. Nevertheless, the counts for each topical entity have not been consistently integrated. As a result, the nodes of the keyword co-occurrence network are large when compared to the number of connections between the entities or terms (i.e., the total number of relationships between co-occurring terms or entities). Additionally, all weighted counts in keywords (or the total link strength of a country/region) should equal the total number of documents (e.g.

Background Spinal surgeries are commonly performed by surgeons from the fields of neurology and orthopedics, and many articles related to the spine have been published by authors in both fields. However, there has been limited research that directly compares the research achievements(RAs) of these two groups of authors. This article conducted a comparative analysis of spine-related RAs between authors in neurology and orthopedics. This study examines differences in spine-related research productivity and impact between neurology and orthopedics authors using these measures, particularly with a novel clustering algorithm.Methods We gathered 2,148 articles written by authors in neurology and orthopedics from the Web of Science core collections, covering the period from 2013 to 2022. To analyze author collaborations(ACs), we employed the follower-leader algorithm (FLA) and conducted cluster analysis. A three-part analysis was carried out: (1) cluster analysis of ACs, (2) mean citation analysis, and (3) a CJAL score based on article category, journal impact factors, authorships, and L-indices. We then utilized R to create visual displays of our findings, including circle bar charts, heatmaps with dendrograms, 4-quadrant radar plots, and forest plots. The mean citations and CJAL scores were compared between authors in neurology and orthopedics.Results When considering first and corresponding authors, orthopedics authors wrote a greater proportion of the articles in the article collections, accounting for 75% (1,600 out of 2,148). The JCAL score based on the top 10 units each also favored orthopedics authors, with 71% (3,626 out of 6,139) of the total score attributed to them. Using the FLA, we observed that orthopedics authors tended to have more author collaborations across countries. Additionally, while citation per article favored orthopedics authors, the mean CJAL score favored neurology authors.Conclusion Authors in orthopedics have a higher number of publications, citations, and CJAL scores related to spine research than those in neurology. Authors in orthopedics tend to have more collaborations and coauthored papers in the field. The study highlights the differences in research productivity and collaboration patterns between the two fields in spine-related research and sheds light on potential contributing factors. The study recommends the use of FLA for future bibliographical studies.

BACKGROUND ChatGPT, a new large language model (LLM) developed by OpenAI, has demonstrated impressive performance in several specialized applications. In spite of the rising popularity and performance of artificial intelligence, there are a limited number of studies that evaluate ChatGPT’s capability for multiple choice questions (NCQs) using KIDMAP of Rasch analysis (i.e., an online tool called KIDMAP, which is used to evaluate the performance of ChatGPT in answering MCQs). OBJECTIVE Study objectives were to (1) demonstrate the use of online Rasch analysis (namely, RaschOnline) and (2) determine the ChatGPT’s grade compared to a normal sample. METHODS ChatGPT capability was evaluated using ten items from Taiwan college entrance examinations for the year 2023. Under a Rasch model, 300 virtual students with normal distributions were simulated and generated to compete with ChatGPR's responses. A total of five visual presentations were created using RaschOnline (e.g., item difficulties, differential item functioning (DIF), item characteristic curve, Wright map, and KIDMAP) to answer the research questions outlined in the objectives. RESULTS The results indicated that (1) the difficulty of the ten items monotonously increased from easier to harder (i.e., -2.43, -1.78, -1.48, -0.64, -0.1, 0.33, 0.59, 1.34, 1.7, and 2.47 logits); (2) there was evidence of DIF between gender groups for item 5(p=0.042); (3) item 5 fits the Rasch model rather well (p=0.61); (4) all of the items fit the Rasch model, as indicated by Infit mean square errors below the threshold of 1.5; (5) there was no significant difference in the measures obtained between gender groups(p=0.832); (6) a significant difference was observed among ability grades(P< 0.001); and (7)ChatGPT's capability is graded as A, surpassing grades B to E. CONCLUSIONS With RaschOnline, we demonstrate that ChatGPT is capable of scoring a grade A compared to a normal sample, with an excellent ability to answer MCQs of English tests for the year 2023 on Taiwan college entrance examinations.

The Hirsch-index (h-index) is a measure of academic productivity that incorporates both the quantity and quality of an author's output. However, it is still affected by self-citation behaviors. This study aims to determine the research output and self-citation rates (SCRs) in the Journal of Medicine (Baltimore), establishing a benchmark for bibliometrics, in addition to identifying significant differences between stages from 2018 to 2021.We searched the PubMed database to obtain 17,912 articles published between 2018 and 2021 in Medicine (Baltimore). Two parts were carried out to conduct this study: the categories were clustered according to the medical subject headings (denoted by midical subject headings [MeSH] terms) using social network analysis; 3 visualizations were used (choropleth map, forest plot, and Sankey diagram) to identify dominant entities (e.g., years, countries, regions, institutes, authors, categories, and document types); 2-way analysis of variance (ANOVA) was performed to differentiate outputs between entities and stages, and the SCR with articles in Medicine (Baltimore) was examined. SCR, as well as the proportion of self-citation (SC) in the previous 2 years in comparison to SC were computed.We found that South Korea, Sichuan (China), and Beijing (China) accounted for the majority of articles in Medicine (Baltimore); ten categories were clustered and led by 3 MeSh terms: methods, drug therapy, and complications; and more articles (52%) were in the recent stage (2020-2021); no significant difference in counts was observed between the 2 stages based on the top ten entities using the forest plot (Z = 0.05, P = .962) and 2-way ANOVA (F = 0.09, P = .76); the SCR was 5.69% (15%); the h-index did not differ between the 2 collections of self-citation inclusion and exclusion; and the SC in the previous 2 years accounted for 70% of the self-citation exclusion.By visualizing the characteristics of a given journal, a breakthrough was made. Subject categories can be classified using MeSH terms. Future bibliographical studies are recommended to perform the 2-way ANOVA and then compare the outputs from 2 stages as well as the changes in h-indexes between 2 sets of self-citation inclusion and exclusion.

Hidradenitis suppurativa (HS) is a chronic, inflammatory and debilitating dermatosis characterized by painful nodules, sinus tracts and abscesses in apocrine gland-bearing areas that predominantly affect women worldwide. New therapeutic interventions based on the clinical manifestations of patients have recently been introduced in numerous articles. However, which countries, journals, subject categories, and articles have the ultimate influence remain unknown. This study aimed to display influential entities in 100 top-cited HS-related articles (T100HS) and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.T100HS data were extracted from PubMed since 2013. Subject categories were classified by MeSH terms using social network analysis. Sankey diagrams were applied to highlight the top 10 influential entities in T100HS from the three aspects of publication, citations, and the composited score using the hT index. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100HS were examined using one-way analysis of variance and regression analysis.The top three countries (the US, Italy, and Spain) accounts for 54% of the T100HS. The T100HS impact factor (IF) is 12.49 (IF = citations/100). Most articles were published in J Am Acad Dermatol (15%; IF = 18.07). Eight subject categories were used. The "methods" was the most frequent MeSH term, followed by "surgery" and "therapeutic use". Saunte et al, from Roskilde Hospital, Denmark, had 149 citations in PubMed for the most cited articles. Sankey diagrams were used to depict the network characteristics of the T100HS. Article citations did not differ by subject category (F(7, 92) = 1.97, P = .067). MeSH terms were evident in the number of article citations predicted (F(1, 98) = 129.1106; P .001).We achieved a breakthrough by displaying the characteristics of the T100HS network on the Sankey diagrams. MeSH terms may be used to classify articles into subject categories and predict T100HS citations. Future studies can apply the Sankey diagram to the bibliometrics of the 100 most-cited articles.

An individual's research domain (RD) can be determined from objective publication data (e.g., medical subject headings and Medical Subject Headings (MeSH) terms) by performing social network analysis. Bibliographic coupling (such as cocitation) is a similarity metric that relies on citation analysis to determine the similarity in RD between 2 articles. This study compared RD consistency between articles as well as their cited references and citing articles (ARCs).A total of 1388 abstracts were downloaded from PubMed and authored by 3 productive authors. Based on the top 3 clusters in social network analysis, similarity in RD was observed by comparing their consistency using the major MeSH terms in author articles, cited references and citing articles (ARC). Impact beam plots with La indices were drawn and compared for each of the 3 authors.Sung-Ho Jang (South Korea), Chia-Hung Kao (Taiwan), and Chin-Hsiao Tseng (Taiwan) published 445, 780, and 163 articles, respectively. Dr Jang's RD is physiology, and Dr Kao and Dr Tseng's RDs are epidemiology. We confirmed the consistency of the RD terms by comparing the major MeSH terms in the ARC. Their La indexes were 5, 5, and 6, where a higher value indicates more extraordinary research achievement.RD consistency was confirmed by comparing the main MeSH terms in ARC. The 3 approaches of RD determination (based on author articles, the La index, and the impact beam plots) were recommended for bibliographical studies in the future.

A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date - Modification]: "2021" [Date - Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs).The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34).Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.

Polycystic kidney disease (PKD) is a genetic disorder in which the renal tubules become structurally abnormal, resulting in the development and growth of multiple cysts within the kidneys. Numerous studies on PKD have been published in the literature. However, no such articles used medical subject headings (MeSH terms) to predict the number of article citations. This study aimed to predict the number of article citations using 100 top-cited PKD articles (T100PKDs) and dissect the characteristics of influential authors and affiliated counties since 2010.We searched the PubMed Central® (PMC) database and downloaded 100PKDs from 2010. Citation analysis was performed to compare the dominant countries and authors using social network analysis (SNA). MeSh terms were analyzed by referring to their citations in articles and used to predict the number of article citations using its correlation coefficients (CC) to examine the prediction effect.We observed that the top 3 countries and journals in 100PKDs were the US (65%), Netherlands (7%), France (5%), J Am Soc Nephrol (21%), Clin J Am Soc Nephrol (8%), and N Engl J Med (6%); the most cited article (PMID = 23121377 with 473 citations) was authored by Vicente Torres from the US in 2012; and the most influential MeSH terms were drug therapy (3087.2), genetics (2997.83), and therapeutic use (2760.7). MeSH terms were evident in the prediction power of the number of article citations (CC = 0.37; t = 3.92; P .01, n = 100).A breakthrough was made by developing a method using MeSH terms to predict the number of article citations based on 100PKDs. MeSH terms are evident in predicting article citations that can be applied to future research, not limited to PKD, as we did in this study.

The article published on 16 May 2021, is well-written and of interest, but remains several questions that are required for clarifications, such as the presentations in Table 1 and Fig. 1 that should be improved further for providing more valuable information to readers. After viewing Table 1, measuring the strength of quantity (= 0.84) referred to the next two counterparts for the top one acronym (e.g., COVID) is demonstrated using the absolute advantage coefficient (AAC). Similarly, Traditional line charts on top-eight acronyms provide us with messages, including (i) DNA and RNA are popular over three decades; (ii) CT, MRI, HIV, SARS, and CoV start in 1972, 1985, 1986, 2003, and 2003, respectively; (iii) the number of COVID substantially surpasses over other seven acronyms in 2020 though the seven acronyms are almost equal in quantity in 2020. We are interested in producing similar Table 1 and Fig. 1 with a video MP4 provided to readers who can click on the link to manipulate the scenarios on their own. We found that the AAC and the traditional line charts on a dashboard make data clear for a better understanding of demonstrating the ascendancy of COVID-19 research using acronyms. The line charts are easily examined on Google Maps.

BACKGROUND It has been common in bibliographical studies to apply the citation trend of research to journal articles. There is no such enhanced temporal bar graph (TBG) to provide a better understanding of articles that are most worth reading. The Sankey-type TBG for highlighting articles worth reading is needed. OBJECTIVE The purpose of this article is to (1) describe the characteristics of articles in JMIR mHealth and uHealth (JMIRMU), (2) illustrate which JMIRMU articles are most worthwhile to read, and (3) compare the differences in article types in terms of citation trends between China and the United States. METHODS On July 11, 2022, we obtained 2,202 abstracts indexed in the Web of Science core collection (WoSCC) by searching for the keywords "JMIR mHealth and uHealth" (Journal) and the time span between 2013 and 2021. Metadata, including author names, research institutes, article identifiers (PMIDs), countries, and article citations, were collected over time. A Newton‒Raphson Iteration Method (NRIM) and a growth/share matrix (GSM) were used to display the burst spot and growth trend. The first and corresponding authors of articles were evaluated and displayed for countries/regions on choropleth maps. Sankey-type TPBs were applied to display (1) the characteristics of articles in JMIRMU, (2) JMIRMU articles that are the most worthwhile for readers, and (3) differences in article types between Chinese and American articles based on citation trends in recent years. RESULTS The top two productive entities were years in 2020(n=566) and 2019(420), the US(1251) and China(341), the University Sydney(Australia)(48), and Seoul National University(South Korea)(42) in research institutes, Ura-Vito Albrecht (Germany)(15) and Melvyn Zhang(Singapore)(10) in authors. Mobile phone (380) and mHealth (755) were the most common author-defined keywords in 2016 and 2021. PMID=29650506 and 30470676 are the most worthy-reading articles, with burst strengths of 5.83 and 6.46, respectively, against the two most-cited articles of 25760773 and 26537656. There was no difference in proportional counts of growth increasing articles between China and the US. CONCLUSIONS Using the Sankey-type TBG and trend analysis, we illustrated how to select articles that are most worth reading for authors. It is recommended for future bibliometric analysis and is not limited to citation analyses of articles in JMIRMU, as we did in this study.

The negative impacts of COVID-19 (ImpactCOVID) on public health are commonly assessed using the cumulative numbers of confirmed cases (CNCCs). However, whether different mathematical models yield disparate results based on varying time frames remains unclear. This study aimed to compare the differences in prediction accuracy between 2 proposed COVID-19 models, develop an angle index that can be objectively used to evaluate ImpactCOVID, compare the differences in angle indexes across countries/regions worldwide, and examine the difference in determining the inflection point (IP) on the CNCCs between the 2 models.Data were downloaded from the GitHub website. Two mathematical models were examined in 2 time-frame scenarios during the COVID-19 pandemic (the early 20-day stage and the entire year of 2020). Angle index was determined by the ratio (=CNCCs at IP÷IP days). The R2 model and mean absolute percentage error (MAPE) were used to evaluate the model's prediction accuracy in the 2 time-frame scenarios. Comparisons were made using 3 visualizations: line-chart plots, choropleth maps, and forest plots.Exponential growth (EXPO) and item response theory (IRT) models had identical prediction power at the earlier outbreak stage. The IRT model had a higher model R2 and smaller MAPE than the EXPO model in 2020. Hubei Province in China had the highest angle index at the early stage, and India, California (US), and the United Kingdom had the highest angle indexes in 2020. The IRT model was superior to the EXPO model in determining the IP on an Ogive curve.Both proposed models can be used to measure ImpactCOVID. However, the IRT model (superior to EXPO in the long-term and Ogive-type data) is recommended for epidemiologists and policymakers to measure ImpactCOVID in the future.

The article published on January 3 in 2017 is well-written and of interest, but remains several questions that are required for clarifications, such as the production of ten clusters of 11,359 journals based on 2015 data using VOSviewer for classification and visualization. After viewing the detailed information about the data in Fig. 1, we are interested in producing the similar Figure on a dashboard with a video MP4 provided to readers who can click on the link to manipulate the scenarios on their own. We found that the process of making diagram of social network analysis on Google Maps using the MS Excel VBA codes. The individual-/cluster-type relations of journals are easily examined on Google Maps.

The h-index has its popularity in the global scientific community. Despite the h-index being used as an indicator of individual research achievement, two main disadvantages have not been solved: (1) all coauthors contributing equally to article bylines and (2) the integer nature of the h-index making it hard to differentiate the IRAs among entities. This article aims to evaluate the most cited authors, institutes, and the US states in ophthalmology since 2010 using a proposed hx-index. Authors who worked for departments of ophthalmology in the United States were selected for identifying their IRAs in Pubmed Central (PMC) since 2010. Using the PubMed search engine, we conducted an observational study of citation analyses in affiliated research institutes and the US states for all authors who worked for departments of ophthalmology since 2010. A total of 18,289 published articles from 46,121 authors related to ophthalmology from the 50 states were analyzed. The bootstrapping method was applied with an estimated 95% confidence interval (CI) to distinguish the differences in IRAs among states and institutes. The x-index and the Kano model were complemental to the h-index for identifying the IRA characteristics and rankings. A pyramid plot was used to illustrate the importance of the author-weighted scheme (AWS) used for evaluating IRAs in academics. The hx-index combined both advantages of h-/x-indexes was proposed to assess IRAs for each facility. A significant difference was identified by observing two bands of estimated 95% CIs that were not overlapped. Furthermore, we drew a choropleth map on Google Maps to visualize the differences of IRA among states. There is a significant rise over time in the number of publications. The top-ranking states in hx-index based on publications and citations were Massachusetts (42.28), California (39.24), and Pennsylvania (34.29). If only the top 100 authors were included for calculating the median hx-index in each state, the top three were California (6.45), Massachusetts (3.97). and New York (3.07), with no significant difference found using the bootstrapping method. The institute and author with the highest hx-index were Harvard Medical School (Massachusetts) and Robert N Weinreb (California), respectively. We demonstrated that Dr. Medeiros from California published 466 articles in PMC and used the example to elucidate the importance of AWS when IRAs were assessed. With an overall increase in publications in the field of ophthalmology, IRAs assessed by these (1) hx-index, (2)the bootstrapping method, and (3) AWS should be synergically and complementarily combined together to evaluate author/institute IRA in the future.

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.

Objective: This study aimed to investigate the journal features by collecting some data from Pubmed Central (PMC) and to interpret the characteristics of the journal for Eur J Cancer Care (Engl) (EJCC). Methods: Selecting 1611 abstracts and their corresponding author names and keywords on September 3, 2017, from PMC, we analyzed data mentioned above to address following features: (1) Nation distribution and author collaborations; (2) Journal features represented by article keywords; (3) The most productive authors and their authorship clusters; (4) The top 10 journals most similar to EJCC. Microsoft Excel VBA routines were programmed to extract data from PMC. Rasch model and SNA Pajek software were used to present visualized displays for EJCC features. Results: We found (1) the majority of the articles are from UK (28%) Australia (10%) and Sweden (5%); (2) The most linked Keywords are cancer and breast cancer; (3) The most productive author is R Sanson-Fisher; (4) The top one journal with the most similarity to EJCC is Support Care Cancer. Conclusion: Social network analysis that provides wide and deep insight into the relationships among nations, coauthor collaborations, abstract keywords and journals most similar to EJCC was performed in this study. The results can be provided to strategy and decision making for the target journal in the future.