Eigenfactor Score Research Articles

The Impact of Preprints on COVID‐19 Research Dissemination: A Quantitative Analysis of Journal Publications

ABSTRACTPreprints have played an unprecedented role in disseminating COVID‐19‐related science results to the public. The study aims to elucidate the role of preprints during the COVID‐19 public health emergency (2020–2023) through a quantitative analysis of journal papers. Among the 247,854 COVID‐19‐related papers published in PubMed, 12,152 were initially released as preprints and were eventually published in 1,380 journals. This number is more than five times the 246 journals to which submissions can be made directly from bioRxiv through the B2J program. Journals with higher impact factors and Normalized Eigenfactor scores tend to publish a larger number of preprint‐derived articles. The proportion of preprints among PubMed papers was 0.049, but this varies significantly by journal. In the top 30 journals, most exceed this proportion, indicating that these journals are preferred by authors for submitting their work. These findings highlight the growing acceptance and impact of preprints in the scientific community, particularly in high‐impact journals.

Evaluation of Health Care Science and Services Journals: Impact Factors and Ranking Indicators

ABSTRACT Assessing the quality of journals in the realm of health care science and services is an intricate and vital pursuit, especially given the swift evolution of health care research amidst global challenges and technological progress. Bibliometric indicators have emerged as indispensable instruments for quantifying and comparing the impact, reach, and significance of scholarly contributions. In this study, a range of bibliometric indicators, including Impact Factor, Eigenfactor Score, Cite Score, and h-index, were utilized. We curated a list of 105 health care science and services journals using data from the Journal Citation Report (JCR) within the Web of Science (WOS), journal metrics from the Scopus database (Cite Score and SNIP), and h5-index from Google Scholar. Additionally, the SCImago Journal Ranking (SJR) was obtained from its official website. Subsequently, Bivariate Pearson’s (r) and Spearman’s (ρ) correlation coefficient tests were conducted to evaluate ranking correlations. The findings revealed strong Pearson’s (r) statistical correlations between JIF and CS (r = 0.908) and between JIF and SJR (r = 0.875), while moderate correlations were observed between JIF and H5-median, H5-index, and SNIP (r = 0.769, r = 0.730, and r = 0.645, respectively). Spearman’s rho analysis indicated high correlations between JIF and CS (ρ = 0.907) and between JIF and SJR (ρ = 0.863), with lower correlations among H5-median, SNIP, and H5-index indicators (ρ = 0.798, ρ = 0.763, and ρ = 0.750, respectively).

Evaluation of Quality Performance and Social Effectiveness of Arts and Humanities Journals Indexed in the Web of Science Database

This study aimed to evaluate the qualitative and Altmetrics status of 147 journals indexed in the Arts and Humanities Citation Index (AHCI). This practical-analytical study used qualitative indicators of journals and quantitative Altmetrics. Data was collected from Web of Science, Journal Citation Reports, and Altmetrics Explorer. The results showed that most of the journals this database covers are from European countries, and only one is from Asian countries. More than half of the reviewed journals have a score lower than average regarding Journal Citation Indicator, Eigenfactor Score, and Article influence score. In addition, 63 % of journals cite articles published less than 19 years ago. Nearly 20 % of journals were not on social media. There is a significant relationship between Journal Citation Indicator indices with Citing Half-life, Article influence score with Eigenfactor Score, and between qualitative and Altmetrics indicators. Most of the reviewed journals showed low-quality performance. These journals needed high scientific impact to increase their credibility and quality and remain in the Web of Science database.

Toward the consolidation of a multi-metric-based journal ranking and categorization system for computer science subject areas

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment.

Comparative bibliometric analysis of leading Open Access journals: a focus on Chinese and non-Chinese journals in science, technology, and medicine

The objective of this study was to examine the scientific performance and internationalization of leading Chinese Open Access (OA) journals in the fields of science, technology, and medicine, specifically those published in English (referred to Chinese journals hereafter). Based on data taken from the Clarivate Analytics and Directory of Open Access Journals (DOAJ), a comparative bibliometric analysis was carried out. Fourteen Chinese journals were sampled, and a total of 22 non-Chinese OA journals were chosen for comparison. The analysis revealed that the majority of the journals were collaboratively published with large international publishers. While Chinese journals demonstrated a steady increase in publication productivity from 2018 to 2020, it remained noticeably lower than that of international journals. Additionally, Chinese journals received fewer citations from highly ranked journals, as evidenced by the three-fold higher Eigenfactor score observed in international journals compared to Chinese journals. Chinese journals exhibited a higher percentage of domestic manuscripts than international journals, as reflected in their elevated Index of National Orientation (INO) values. This study contributes to a better understanding of the scientific performance and internationalization of Chinese journals in the global publishing landscape, while also identifying potential areas for improvement.

Synthesis of Publication Metrics in Kinesiology-Related Journals: Proxies for Rigor, Usage, and Prestige

ABSTRACT This study documented three domains of journal research quality using metrics from six major publishers and two major database services for a large sample of kinesiology-related journals: Rigor/soundness, usage, and prestige. All journal metrics were highly skewed and variable. Median acceptance rates and initial review time were consistent with rigorous peer-review. Four variables were strongly interrelated (0.849 < r < 0.963) and indicate typical usage of 1 to 3 citations for articles in these journals. The 5-year Eigenfactor Score had a relatively weaker association (0.510 < r < 0.758) with the usage metrics, supporting it as an estimate of the prestige afforded these kinesiology journals in Scopus instead of usage. Care must be taken to interpret multiple journal metrics; taking into account the skew, large variability, and confounding factors such as journal size and subject. Differences across quality domains, metrics, and bias from numerous contextual factors refute the insidious misuse of any single journal metric as quality indictor for ranking journals.

Evaluating and Comparing the Quality of the Agronomy and Crop Science Journals Using Bibliometric Indicators

ABSTRACT The field of agronomy and crop science plays a crucial role in ensuring global food security and sustainable agricultural practices. In the realm of scholarly publications, bibliometric indicators have emerged as invaluable tools for quantifying and comparing the impact, reach, and significance of such contributions. Various bibliometric indicators were employed for the study, including Impact Factor, Eigenfactor Score, Cite Score, and h-index. To accomplish this, we compiled a roster of 90 agronomy and crop science journals using the Journal Citation Report (JCR) within the Web of Science (WOS), journal metrics from the Scopus database (Cite Score and SNIP), and h5-index from Google Scholar. The SCImago Journal Ranking (SJR) was obtained from its official website. Subsequently, we conducted Bivariate Pearson’s (r) and Spearman’s (ρ) correlation coefficient tests to assess ranking correlations. The study revealed notable positive correlations particularly, a strong Pearson’s (r) correlation evident between the Impact Factor (JIF) and both the Cite Score (CS) and SCImago Journal Rank (SJR) indicators, yielding values of (r = 0.962) and (r = 0.932), respectively. Interestingly, despite the distinct mathematical foundations underpinning the various citation-based bibliometric indices scrutinized, our findings underscored their robust correlations when it comes to ranking agronomy and crop science journals.

Dermatology journals from India: A critical appraisal of the journal metrics.

Background Bibliometrics refer to documents and citation-based measures that measure different aspects of performance of a journal, including impact, output and prestige. Objective The aim of this study was to collect bibliometric data of various Indian dermatology journals as well as Indian journals from other disciplines, in order to compare relative performances. Methods Journal metrics pertaining to various Indian journals, both from dermatology [Indian Journal of Dermatology, Venereology and Leprology (IJDVL), Indian Journal of Dermatology (IJD), Indian Dermatology Online Journal, Indian Journal of Pediatric Dermatology and International Journal of Trichology] and other disciplines [Indian Journal of Medical Research (IJMR), Indian Journal of Pediatrics (IJP), Indian Journal of Ophthalmology and Indian Journal of Pharmacology] were sought. Data pertaining to the following 8 metrics during the year 2021 was collected: Journal Impact factor, SCImago Journal Rank, h5-index, Eigenfactor score and normalized Eigenfactor Score, Journal Citation Indicator, Scimago Journal and Country Rank H-index, CiteScore and Source Normalized Impact per Paper. Results Among Indian dermatology journals, for the year 2021, IJDVL had the highest impact factor (2.217) and h-index (48). IJD led in terms of prestige metrics such as SCImago Journal Rank (0.403), Eigenfactor score (0.00231) and Source Normalized Impact per Paper (1.132). IJDVL underperformed with respect to an average dermatology journal on all three prestige metrics. Among selected journals from other disciplines, two (IJMR and IJP) had impact factor exceeding five, despite lagging behind IJDVL two years ago. Most had normalized scores exceeding 1, indicating better performance than an average journal from their respective fields. Limitations Non-inclusion of altmetrics related data Conclusion IJDVL is one of the leading Indian journals in the field of dermatology, followed closely by IJD. A rise in IJDVL influence is evident over the past decade, as evident by various metrics. However, the progress still trails behind the average of global dermatology journals as evident by the field-normalized journal metrics, indicating potential for further growth of journal influence.

The 50 most-cited articles on temporomandibular disorders: A bibliometric analysis.

Temporomandibular disorders (TMD) are musculoskeletal conditions involving masticatory muscles and temporomandibular joints. Bibliometric analysis has been introduced as a new method for collecting and analyzing information on scientific articles. The aim of this study was to identify the 50 most cited articles on TMD, performing a bibliometric analysis of the identified papers to favor research and clinical practice. On December 17, 2021 a systematic research was performed to find all papers on TMD published in the literature. The Incites Journal Citation Reports dataset and Scopus database was used to obtain bibliometric indexes of the authors and metrics data of the journals, including Impact Factor, Eigenfactor Score, and Normalized Eigenfactor. VOSviewer was used to visualize the keyword mapping networking, with the nodes standing for keywords and the edges for keyword relationships. Out of 24372 papers obtained by the search strategy, the 50 most cited articles on TMD were analyzed. The paper with the highest number of citations (n= 3020) was on TMD research diagnostic criteria. Rate of citations per year collected by the 50 most cited articles on TMD has been increasing over time (from 627 in 2006 to 1483 in 2021). Most of them were cross-sectional studies (n= 20; 40.0%) and narrative reviews (n= 14; 28.0%). This bibliometric study showed an increasing number of citations for articles on TMD, testifying a high interest in the last years. Starting from this analysis, future studies might provide high-quality evidence on TMD management.

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression. Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.

Determinants of impact factor and Eigenfactor score in otolaryngology journals.

This study aims to identify determinants of high impact, measured by Impact Factor (IF) and Eigenfactor score, among otolaryngology journals. Bibliometric data of "otorhinolaryngology" journals were collected from the Journal Citation Reports (JCR) database. For the years 2009-2020, we collected normalized Eigenfactor score, 5-year IF, immediacy index, fraction of IF from journal-self citation, proportion and magnitude of published citable articles, and total citation counts. High-IF and -Eigenfactor journals were considered those within the top-quartile of that metric each respective year. High-IF and -Eigenfactor otolaryngology journals displayed higher 5-year IFs, immediacy indexes, and IF without self-citation (p < .05 for all years) including total citations counts and citable articles when ranked by Eigenfactor (p < .05 for all years). Otolaryngology IF correlated with 5-year IF and immediacy index within the same year (p < .05 for all years) and from previous years (p < .05 for all years; p < .05 for2017-2018; p > .05 for2009-2016). Eigenfactor correlated with 5-year IF, total citation counts, and citable articles within the same year (p < .05 for all years) and previous years (p < .05 for2013-2018). Multilinear regression revealed that 5-year IF (p < .05 for2009-2018) and immediacy index from the prior 2 years (p < .05 for2017-2018; p > .05 for2009-2016) predicted 2019 IF. Similarly, 5-year IF, total citation counts, and citable articles (p < .05 for 2013-2018) predicted 2019 Eigenfactor score. Sustained publication of impactful articles is the dominant driver of high IF and Eigenfactor score. Eigenfactor score reflects a unique evaluation of otolaryngology journals; ranking otolaryngology journals by their Eigenfactor scores significantly alters journal ranking compared to ranking by IF. NA.

34860 Levels of evidence within dermatology; bibliometric trends compared with general medicine and general surgery from 2008-2017: A bibliometric systematic review

Despite adaptation of high-evidence guidelines, such as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), low-reporting quality persists in scientific publications across specialties, including dermatology. A bibliometric systematic review was conducted to compare level 1 evidence publications in Dermatology, General Surgery and General Medicine from 2008-2017. Publications were identified through Clarivate’s 2016 Journal Citation Reports (JCR) and PubMed (1/1/2008-12/31/2017) in accordance with PRISMA. Three JCR categories were selected: General Medicine, General Surgery, and Dermatology. The top 10 medical journals in each category, based on 2016 JCR Eigenfactor score, were included. Publications were categorized by article-type, “Systematic Reviews (SRs),” “Meta-Analyses (MAs),” and “Randomized Controlled Trials (RCTs)” and compared from ‘2008-2012’ and ‘2013-2017.’ 127,796 publications were identified: 11,990 SRs, MAs, and RCTs were included. Data analysis revealed that high-evidence publications in Dermatology from 2008-2012 to 2013-2017 increased by 25.03% (7.91% vs. 9.89%); General Surgery increased by 24.68% (6.97% vs. 8.69%); and General Medicine decreased by 10.32% (12.02% vs. 10.78%). There was a statistically significant change in the percentage of SRs (P = .0071) and MAs (P = .0142) in Dermatology, as well as SRs (P = .0053) in General Surgery. Cross-specialty analysis between 2008-2012 and 2013-2017 showed statistically significant differences in SRs and MAs between General Medicine and Dermatology (P = .0049), and General Medicine and Surgery (P = .0038). Dermatology has shown significant growth in high-evidence publications, comparable to General Surgery, and approaching the total percentage of high-evidence General Medicine publications. While this is promising, examining the quality of these publications and striving for increased evidence in dermatology publications is imperative.

Editorial Outstanding Associate Editors and Top Reviewers

The IEEE Transactions on Antennas and Propagation is one of the most influential publications of IEEE. Its scientific contributions attract a lot of interest and are among the most downloaded across all IEEE publications. In 2021, the IEEE Transactions on Antennas and Propagation ranked third out of 389 publications available on IEEE Xplore for total number of downloads. The IEEE Transactions on Antennas and Propagation ranks second for total number of downloads out of all IEEE publications, when open access journals are excluded. In addition, the bibliometric measures released by Clarivate for year 2021 indicate that the journal performance has set a new record in its history, with an impact factor of 4.824, a 5-year impact factor that reached 5.005, an eigenfactor score of 0.041, and article influence score of 1.048.

A comparison of scientometric data and publication policies of ophthalmology journals.

Purpose:This retrospective database analysis study aims to present the scientometric data of journals publishing in the field of ophthalmology and to compare the scientometric data of ophthalmology journals according to the open access (OA) publishing policies.Methods:The scientometric data of 48 journals were obtained from Clarivate Analytics InCites and Scimago Journal & Country Rank websites. Journal impact factor (JIF), Eigenfactor score (ES), scientific journal ranking (SJR), and Hirsch index (HI) were included. The OA publishing policies were separated into full OA with publishing fees, full OA without fees, and hybrid OA. The fees were stated as US dollars (USD).Results:Four scientometric indexes had strong positive correlations; the highest correlation coefficients were observed between the SJR and JIF (R = 0.906) and the SJR and HI (R = 0.798). However, journals in the first quartile according to JIF were in the second and third quartiles according to the SJR and HI and in the fourth quartile in the ES. The OA articles published in hybrid journals received a median of 1.17-fold (0.15–2.71) more citations. Only HI was higher in hybrid OA; other scientometric indexes were similar with full OA journals. Full OA journals charged a median of 1525 USD lower than hybrid journals.Conclusion:Full OA model in ophthalmology journals does not have a positive effect on the scientometric indexes. In hybrid OA journals, choosing to publish OA may increase citations, but it would be more accurate to evaluate this on a journal basis.

Trends in the Extracorporeal Membrane Oxygenation Literature: A Bibliometric Analysis in the COVID-19 Era.

Extracorporeal Membrane Oxygenation (ECMO) was first used in the 1970s. Its use is increasingly common in critical care and perioperative settings and has gained newfound prominence during COVID-19. To guide future research, we conducted a bibliometric analysis of ECMO literature. Thomson Reuters Web of Science was searched to March 7, 2021. Articles were ranked by total number of citations. Data was extracted from the 100 most cited papers relevant to ECMO for study design, topic, author, year, and institution. Journal impact factor for 2019 and Eigenfactor scores were also recorded. Our search retrieved a total of 18,802 articles. Median number of citations for the top 100 articles was 220 (range 157-1,819). These were published in 34 journals, with first authors originating from 15 countries. The Annals of Thoracic Surgery had the highest number of articles (n = 9) while Lancet publications had the most citations (n = 3,191). Use of ECMO was most commonly observed in cardiogenic shock or acute respiratory distress syndrome. United States had the greatest article output (n = 49). With 10 publications, 2013 was the most prolific year. Using linear regression, when controlled for time since publication, there was no statistically significant relationship between 2019 journal impact factor and number of article citations (p = .09). Top articles in the ECMO literature are of considerable impact and quality. As the United States produced the bulk of the prominent evidence base, and most data were regarding respiratory issues, outsized advances in ECMO may be possible within the United States during the COVID-19 era.

Trends in the Extracorporeal Membrane Oxygenation Literature: A Bibliometric Analysis in the COVID-19 Era

Extracorporeal Membrane Oxygenation (ECMO) was first used in the 1970s. Its use is increasingly common in critical care and perioperative settings and has gained newfound prominence during COVID-19. To guide future research, we conducted a bibliometric analysis of ECMO literature. Thomson Reuters Web of Science was searched to March 7, 2021. Articles were ranked by total number of citations. Data was extracted from the 100 most cited papers relevant to ECMO for study design, topic, author, year, and institution. Journal impact factor for 2019 and Eigenfactor scores were also recorded. Our search retrieved a total of 18,802 articles. Median number of citations for the top 100 articles was 220 (range 157–1,819). These were published in 34 journals, with first authors originating from 15 countries. The Annals of Thoracic Surgery had the highest number of articles (n = 9) while Lancet publications had the most citations (n = 3,191). Use of ECMO was most commonly observed in cardiogenic shock or acute respiratory distress syndrome. United States had the greatest article output (n = 49). With 10 publications, 2013 was the most prolific year. Using linear regression, when controlled for time since publication, there was no statistically significant relationship between 2019 journal impact factor and number of article citations (p = .09). Top articles in the ECMO literature are of considerable impact and quality. As the United States produced the bulk of the prominent evidence base, and most data were regarding respiratory issues, outsized advances in ECMO may be possible within the United States during the COVID-19 era.

Peer Review Metrics and their influence on the Journal Impact

The manuscript processing timeline, a necessary facet of the publishing process, varies from journal to journal, and its influence on the journal impact needs to be studied. The current research looks into the correlation between the ‘Peer Review Metrics’ (submission to first editorial decision; submission to first post-review decision and submission to accept) and the ‘Journal Impact Data’ (2-year Impact Factor; 5-year Impact Factor; Immediacy Index; Eigenfactor Score and Article Influence Score). The data related to ‘Peer Review Metrics’ (submission to first editorial decision; submission to first post-review decision and submission to accept) and ‘Journal Impact Data’ (2-year Impact Factor; 5-year Impact Factor; Immediacy Index; Eigenfactor Score and Article Influence Score) were downloaded from the ‘Nature Research’ journals website https://www.nature.com/nature-portfolio/about/journal-metrics . Accordingly, correlations were drawn between the ‘Peer Review Metrics’ and the ‘Journal Impact Data’. If the time from ‘submission to first editorial decision’ decreases, the ‘Journal Impact Data’ increases and vice versa. However, an increase or decrease in the time from ‘submission to first editorial decision’ does not affect the ‘Eigenfactor Score’ of the journal and vice versa. An increase or decrease in the time from ‘submission to first post-review decision’ does not affect any ‘Journal Impact Data’ and vice versa. If the time from ‘submission to acceptance’ increases, the ‘Journal Impact Data’ (2-year Impact Factor, 5-year Impact Factor, Immediacy Index and Article Influence Score) also increases, and if the time from ‘submission to acceptance’ decreases, so will the ‘Journal Impact Data’. However, an increase or decrease in the time from ‘submission to acceptance’ does not affect the ‘Eigenfactor Score’ of the journal and vice versa. The study will act as a ready reference tool for the scholars to select the most appropriate submitting platforms for their scholarly endeavours. Furthermore, the performance and evaluative indicators responsible for a journal’s overall research performance can also be understood from a micro-analytical view, which will help the researchers select appropriate journals for their future scholarly submissions. Lengthy publication timelines are a big problem for the researchers because they are not able to get the credit for their research on time. Since the study validates a relationship between the ‘Peer Review Metrics’ and ‘Journal Impact Data’, the findings will be of great help in making an appropriate journal’s choice. The study can be an eye opener for the journal administrators who vocalise a speed-up publication process by enhancing certain areas of publication timeline. The study is the first of its kind that correlates the ‘Peer Review Metrics’ of the journals and the ‘Journal Impact Data’. The study’s findings are limited to the data retrieved from the ‘Nature Research’ journals and cannot be generalised to the full score of journals. The study can be extended across other publishers to generalise the findings. Even the articles’ early access availability concerning ‘Peer Review Metrics’ of the journals and the ‘Journal Impact Data’ can be studied.

The Quality of Otology and Neurotology Research in Otology Journals.

To establish the level of evidence and publishing trends in otology-specific journals over a 20-year period. Retrospective analysis. The three O/N specific journals with the highest Eigenfactor scores were identified. All articles published in the years 1998, 2008, and 2018 were reviewed and level of evidence (LoE) based on standards set by the Oxford Centres for Evidence Based Medicine was assigned by two independent reviewers. One way analysis of variance and 95% bootstrap sensitivity analysis were performed. A total of 1,062 studies were published over 20 years, of these 809 (76.2%) were eligible for inclusion in the present study. The average number of publications per year increased over time. The average LoE improved significantly over the total interval (-0.235, p = 0.027, [CI -0.45, -0.019]), however did not between 1998 and 2008 (p = 0.111) or between 2008 and 2018 (p = 1). When looking at just LoE 1 or 2, the number and percentage of higher quality papers improves over time-48/158 (30.4%) in 1998, rising to 94/250 (37.6%) in 2008, and 158/401 (39.4%) in 2008. Over the past two decades there has been an overall increase in the quantity and quality (as measured by LoE) of publications in O/N-specific journals. In general, quality of O/N studies is slightly better in O/N-specific journals compared with general OHNS journals. However, there are still improvements to be had in the proportion of high-evidence publications, as they still number less than half of all total publications in the subspecialty.

Social Media and Academic Impact: Do Early Tweets Correlate with Future Citations?

This study aims to determine whether social media platforms (SMPs) can influence article impact as measured by citations. This is a cross-sectional study that analyzed articles published in the top ten otolaryngology journals by the Eigenfactor score in January 2015. Total accumulated twitter mentions and citations were recorded in 2021. The main outcomes examined the difference in citations, tweets, article types, and author counts accumulated over a 5-year period for all articles that were either tweeted or non-tweeted. 3094 articles were included for analysis. The average article was cited 11.2 ± 13.2 times and tweeted 2.10 ± 4.0 times. Sixty-four percent of the articles had at least one tweet. Over the study period, there was a statistically significant difference in mean number of citations between tweeted articles (12.1 ± 15.0) vs non-tweeted articles (9.6 ± 10.5) citations, representing a 26% difference (P < .001). Review articles had the highest mean citations (19.4 ± 23.4), while editorials had the lowest mean citations (2.8 ± 6.9). Tweets peaked in the year of publication, but citations continued to rise in the subsequent years. Tweeted articles' peak citation rate change was +1.27 mean citations per year, compared to +.99 mean citations per year in non-tweeted articles. The mean author count in tweeted articles (5.40 ± 3.1) was not significantly different than mean author count in non-tweeted articles (5.19 ± 2.65, P = .0794). These data suggest a moderate correlation between tweets and article citations, but a clear difference in the number of citations in articles tweeted vs those with no tweets. Thus, dissemination of knowledge may be impacted by social media platforms such as Twitter.

Bibliometrics Analysis of Diagnostic Test Accuracy Studies of Bladder Cancer.

Background Bladder cancer is amongst the top 10 most common neoplasms worldwide. It is responsible for over 150,000 deaths per annum. It is of great importance to study its clinical diagnosis and management. As a literature synthesis technique, bibliometrics analysis helps us to take our knowledge of bladder cancer diagnosis research, topics, and trends a step further. It is critical to elucidate the literature landscape and its pertinent impact on this field, in order to have a better understanding and improved management of bladder cancer. Materials and Methods Search terms related to bladder cancer diagnosis were used to retrieval publications which met the predefined criteria in the Scopus database developed by Elsevier. Included articles were further evaluated by year of publication, country, language, institution, article type, source journal, coauthorship networks, and text mining of titles. The R software, as well as the tm, ggplot2 packages, and the VOSviewer (version 1.6.15) were used as software tools. Results A comprehensive literature search in the Scopus database returned a total of 824 publication items. Among them, 601 (72.94%) were published as journal articles, and 117 (14.20%) were published as reviews. The number of annual publications in this field has been increasing in recent years. English represents the language used in the majority of the articles (634, 77.13%). The highest number of citations received for a single article is as high as 628 times. We also clustered and visualized a network of 701 authors with no less than 3 publications. The term cloud and hierarchical clustering dendrogram which revealed with text mining were used to discover the hot research topics in this field. We also statistically analyzed evolution of the major journal impact indices, including Impact Factor, EigenFactor Score, and CiteScore of 11 journals with a period of 9 years. Conclusion Through bibliometrics analysis, we found hot topics and related research focuses and trends in bladder cancer diagnosis. This bibliometrics analysis has identified influential articles in the field and provides a useful guide to researchers as to what type of article constitutes a highly citable publication in this subject. In addition, a coauthorship network helps researchers find out which team may be a potential partner and where their studies focus on.