Top Journals Research Articles

Research on aerobic fitness in children and adolescents: a bibliometric analysis based on the 100 most-cited articles.

This study aims to conduct a bibliometric analysis of the 100 most-cited articles to examine research trends, hot topics, and gaps in aerobic fitness research in children and adolescents, addressing the lack of evidence synthesis. The Web of Science Core Collection database was used for literature search, and bibliometric characteristics of the included research articles were imported and calculated. Descriptive statistics and visualizations by the VOS viewer were used for the presentation of bibliometric characteristics. The 100 most cited articles received an average of 104 citations. British Journal of Sports Medicine and Medicine and Science in Sports and Exercise were the two top journals that published aerobic fitness research in children and adolescents. The United States was the top country that contributed to the most-cited research articles. Three top research topics were identified from the analysis, such as neuroscience, developmental psychology, and aerobic health. Aerobic fitness research in children and adolescents has received much attention and interest since 2000. The most contributing authors in this research field were from developed countries, such as the United States, and cognition and health-related research were priorities.

A Bibliometric Analysis of Green Human Resource Management

The purpose of this investigation is to use bibliometric analysis to examine publication trends, citations, top authors, top journals, and top study fields in the framework of Green Human Resource Management (HRM). This study analysed the data from 362 research studies between 2014 and 2024 using the bibliometric review approach. The VOS viewer program was used to analyse the data once it was gathered from the Dimensions database. Only two papers were published in 2014; by 2024, there will be sixty papers, according to the research. Moreover, there has been a significant increase in publications on Green HRM between 2019 and 2023. According to citation analysis, the study has determined the following information: the most significant studies on green human resource management (HRM), the most prestigious journals that publish articles on HRM, the most well-known authors who publish articles on HRM, and the top subject areas that publish research on HRM. This study show that the issue of green human resource management (HRM) is interdisciplinary in nature, as it is linked to fields such as biological sciences, engineering, psychology, and neuroscience, in addition to management and commerce. The review aims to provide scholars with an overview of Green HRM and highlight relevant areas for additional study. Keywords: Green HRM, Green Human Resource Management, Bibliometric, Citation analysis, Top journals

Research impacts and trends in feedback literacy: a bibliometric analysis

Given its prominence in maximising feedback processes, feedback literacy has received large scholarly attention over the past decade. This article aims to use a bibliometric approach to provide an overview of the evolution of feedback literacy from 2012 to May 1, 2024. Based on 240 documents retrieved from Scopus, a performance analysis and science mapping were conducted to explore research trends and impacts in feedback literacy. The performance analysis identifying volume, key authors, principal journals, and leading institutions reveals the research impacts in the feedback literacy landscape. The science mapping adopted the bibliometric software, VOSviewer, to analyse keyword co-occurrences and co-citations and visualised research trends in feedback literacy. Findings reveal two key authors, David Carless and David Boud, a top principal journal, Assessment and Evaluation in Higher Education, and two leading institutions, the University of Hong Kong and Deakin University leading the feedback literacy field. It also reveals important themes in feedback literacy research: student and teacher feedback literacy, peer feedback and assessment, ecological perspectives, development and validation of feedback literacy scales, and L2 writing. This paper sheds light on the current state of feedback literacy research and provides insights and trajectories for different stakeholders in the education sector.

The E-leadership transformation in corporate environment: A bibliometric study

BACKGROUND: This study delves into the academic literature regarding the significance of e-leadership transformation within corporate environments. OBJECTIVE: The primary objective is to analyze and synthesize existing research on e-leadership transformation, identifying key trends, contributors, and thematic clusters. The study aims to provide a comprehensive understanding of the processes and impacts associated with e-leadership, as well as highlight areas for future research. METHODS: We used a dual-method approach incorporating bibliometric analysis as a part of the Systematic Literature Review (SLR) to examine 269 journal articles published between 2010 and 2024, with a focus on the period from 2019 onwards. RESULTS: The analysis identifies significant trends, influential articles, top journals, authors, and leading countries in the field. We identify thematic clusters such as digital leadership and strategic transformation, organisational communication and performance frameworks, behavioural dynamics, and transformational leadership strategies. IMPLICATIONS: Insights from this study offer a deeper understanding of e-leadership transformation’s relevance in corporate settings, highlighting future research prospects and avenues for further exploration in this dynamic and evolving field.

Exploring the implicit meanings of 'cultural diversity': a critical conceptual analysis of commonly used approaches in medical education.

Existing approaches to cultural diversity in medical education may be implicitly based on different conceptualisations of culture. Research has demonstrated that such interpretations matter to practices and people concerned. We therefore sought to identify the different conceptualisations espoused by these approaches and investigated their implications for education. We critically reviewed 52 articles from eight top medical education journals and subjected these to a conceptual analysis. Via open coding, we looked for references to approaches, their objectives, implicit notions of culture, and to implementation practices. We iteratively developed themes from the collected findings. We identified several approaches to cultural diversity teaching that used four different ways to conceptualise cultural diversity: culture as 'fixed patient characteristic', as 'multiple fixed characteristics', as 'dynamic outcome impacting social interactions', and as 'power dynamics'. We discussed the assumptions underlying these different notions, and reflected upon limitations and implications for educational practice. The notion of 'cultural diversity' challenges learners' communication skills, touches upon inherent inequalities and impacts how the field constructs knowledge. This study adds insights into how inherent inequalities in biomedical knowledge construction are rooted in methodological, ontological, and epistemological principles. Although these insights carry laborious implications for educational implementation, educators can learn from first initiatives, such as: standardly include information on patients' multiple identities and lived experiences in case descriptions, stimulate more reflection on teachers' and students' own values and hierarchical position, acknowledge Western epistemological hegemony, explicitly include literature from diverse sources, and monitor diversity-integrated topics in the curriculum.

Do infographics ‘spin’ the findings of health and medical research?

ObjectiveTo compare the prevalence of ‘spin’, and specific reporting strategies for spin, between infographics, abstracts and full texts of randomised controlled trials (RCTs) reporting non-significant findings in the field of...

Analyzing the energy markets and financial markets linkage: A bibliometric analysis and future research agenda

AbstractThis study has been conducted using 200 articles from 1984 to 2023, by undertaking a meta‐literature review on the subject of energy markets and financial markets linkage (EFML). Our study consists of content analysis of 200 articles along with other analyses such as (i) co‐citation analysis, (ii) co‐authorship analysis, and (iii) cartographic analysis. We have identified five research streams: (1) Oil, stock, commodities markets, crypto currency, real estate, exchange rate linkage; (2) Oil and stock market linkage; (3) Energy as a portfolio diversification tool; (4) Dynamic linkage between the oil‐commodity market and stock market; and (5) Oil and other market linkages with respect to the COVID‐19 pandemic, the subsequent financial crisis, and other events. We have performed a comprehensive review of the EFML literature and provided the influential aspects of top journals and authors, the characteristics of the most studied topics, past and current key research streams in the EFML literature. We have also suggested 63 future research questions. Looking at the widespread interest of finance scholarship in the area of energy market linkages, the assessment of energy market linkages, and possible portfolio diversification opportunities and benefits, is of great importance and interest to researchers, policy holders, and portfolio managers.

A Bibliometric Analysis of Green Financing and Renewable Energy Research for 2000-2023

This bibliometric analysis examines the research on green financing and renewable energy from 2000 to 2023. ​The study analyses the publication trends, most productive countries and institutions, frequently occurring concepts, and active and cited authors in this field. The findings reveal a significant increase in publications on green financing and renewable energy in recent years which highlights the increasing interest and investment in these fields, with China being the most productive country. The analysis of keywords highlights the focus on renewable energy, sustainability, and climate change. ​ The study also identifies the most active and cited authors, as well as the top journals in this research area. Overall, the analysis underscores the growing interest and importance of green financing and renewable energy in addressing climate change and promoting sustainable development. ​The analysis presents future opportunities for research in the areas of green financing and renewable energy, as well as the development of sustainable finance and renewable energy solutions.

Evaluating guideline and registration policies among neurology journals: a cross-sectional analysis

BackgroundNeurological disorders have had a substantial rise the last three decades, imposing substantial burdens on both patients and healthcare costs. Consequently, the demand for high-quality research has become crucial for exploring effective treatment options. However, current neurology research has some limitations in terms of transparency, reproducibility, and reporting bias. The adoption of reporting guidelines (RGs) and trial registration policies has been proven to address these issues and improve research quality in other medical disciplines. It is unclear the extent to which these policies are being endorsed by neurology journals. Therefore, our study aims to evaluate the publishing policies of top neurology journals regarding RGs and trial registration.MethodsFor this cross-sectional study, neurology journals were identified using the 2021 Scopus CiteScore Tool. The top 100 journals were listed and screened for eligibility for our study. In a masked, duplicate fashion, investigators extracted data on journal characteristics, policies on RGs, and policies on trial registration using information from each journal’s Instruction for Authors webpage. Additionally, investigators contacted journal editors to ensure information was current and accurate. No human participants were involved in this study. Our data collection and analyses were performed from December 14, 2022, to January 9, 2023.ResultsOf the 356 neurology journals identified, the top 100 were included into our sample. The five-year impact of these journals ranged from 50.844 to 2.226 (mean [SD], 7.82 [7.01]). Twenty-five (25.0%) journals did not require or recommend a single RG within their Instructions for Authors webpage, and a third (33.0%) did not require or recommend clinical trial registration. The most frequently mentioned RGs were CONSORT (64.6%), PRISMA (52.5%), and ARRIVE (53.1%). The least mentioned RG was QUOROM (1.0%), followed by MOOSE (9.0%), and SQUIRE (17.9%).ConclusionsWhile many top neurology journals endorse the use of RGs and trial registries, there are still areas where their adoption can be improved. Addressing these shortcomings leads to further advancements in the field of neurology, resulting in higher-quality research and better outcomes for patients.

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BackgroundMissing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (“missing completely at random”, “missing at random” [MAR], “missing not at random”) are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.MethodsWe searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.ResultsOf the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.ConclusionEffort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks

AbstractA new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of the binary variables indicating if each single actor is involved or not in an event. The adopted parametrization is based on first- and second-order effects, formulated as in marginal models for categorical data and free higher order effects. In particular, second-order effects are log-odds ratios with meaningful interpretation from the social perspective in terms of tendency to cooperate, in contrast to first-order effects interpreted in terms of tendency of each single actor to participate in an event. These effects are parametrized on the basis of the event times, so that suitable latent trajectories of individual behaviors may be represented. Inference is based on a composite likelihood function, maximized by an algorithm with numerical complexity proportional to the square of the number of units in the network. A classification composite likelihood is used to cluster the actors, simplifying the interpretation of the data structure. The proposed approach is illustrated on simulated data and on a dataset of scientific articles published in four top statistical journals from 2003 to 2012.

Research landscape analysis on dual diagnosis of substance use and mental health disorders: key contributors, research hotspots, and emerging research topics

BackgroundSubstance use disorders (SUDs) and mental health disorders (MHDs) are significant public health challenges with far-reaching consequences on individuals and society. Dual diagnosis, the coexistence of SUDs and MHDs, poses unique complexities and impacts treatment outcomes. A research landscape analysis was conducted to explore the growth, active countries, and active journals in this field, identify research hotspots, and emerging research topics.MethodA systematic research landscape analysis was conducted using Scopus to retrieve articles on dual diagnosis of SUDs and MHDs. Inclusion and exclusion criteria were applied to focus on research articles published in English up to December 2022. Data were processed and mapped using VOSviewer to visualize research trends.ResultsA total of 935 research articles were found. The number of research articles on has been increasing steadily since the mid-1990s, with a peak of publications between 2003 and 2012, followed by a fluctuating steady state from 2013 to 2022. The United States contributed the most articles (62.5%), followed by Canada (9.4%). The Journal of Dual Diagnosis, Journal of Substance Abuse Treatment, and Mental Health and Substance Use Dual Diagnosis were the top active journals in the field. Key research hotspots include the comorbidity of SUDs and MHDs, treatment interventions, quality of life and functioning, epidemiology, and the implications of comorbidity. Emerging research topics include neurobiological and psychosocial aspects, environmental and sociocultural factors, innovative interventions, special populations, and public health implications.ConclusionsThe research landscape analysis provides valuable insights into dual diagnosis research trends, active countries, journals, and emerging topics. Integrated approaches, evidence-based interventions, and targeted policies are crucial for addressing the complex interplay between substance use and mental health disorders and improving patient outcomes.

Evaluation of Top Journals and Authors in Behavioral Economics: A Bibliometric Approach

Behavioral economics has become an essential subfield within economics, integrating psychological insights to better understand decision-making processes. This study conducts a comprehensive bibliometric analysis to evaluate the evolution of behavioral economics, identify key research themes, and map the contributions of influential authors. Using data sourced from the Google Scholar database, the study analyzes publication trends, co-authorship networks, and the temporal development of research areas. The findings reveal that core concepts such as behavioral finance, public policy, and experimental economics continue to dominate the field, while emerging areas like consumer behavior and insurance indicate new research directions. The co-authorship analysis highlights the importance of collaboration among scholars in shaping the field's development. These insights provide valuable implications for researchers, educators, policymakers, and funding bodies in guiding future research, curriculum development, and resource allocation. This study contributes to a deeper understanding of the intellectual structure and trajectory of behavioral economics.

Diffusion Magnetic Resonance Imaging and Human Reward System Research: A Bibliometric Analysis and Visualisation of Current Research Trends.

The human reward system has been extensively studied using neuroimaging. This bibliometric analysis aimed to determine the global trend in diffusion magnetic resonance imaging (dMRI) and human reward research in terms of the number of documents, the most active countries and their collaborating countries, the top journals and institutions, the most prominent authors and most cited articles, and research hotspots. The research datasets were acquired from the Scopus database. The search terms used were 'reward' AND 'human' AND 'diffusion imaging' OR 'diffusion tensor imaging' OR 'diffusion MRI' OR 'diffusion-weighted imaging' OR 'tractography' in the abstract, article title and keywords. A total of 336 publications were analysed using Harzing's Publish or Perish and VOSviewer software. The results revealed an upward trend in the number of publications with the highest number of articles in 2020 and 2022. Most publications were limited to countries, authors, and institutions in the USA, China and Europe. Bracht, Coenen, Wiest, Federspiel and Feng were among the top authors from Switzerland, Germany and the UK. Neuroimage, Neuroimage Clinical, Frontiers in Human Neuroscience, Human Brain Mapping, and the Journal of Neuroscience were the top journals. Among the top articles, six were reviews and four were original articles, while the top keywords in human reward research were 'diffusion MRI', 'adolescence', 'depression' and 'reward-related brain areas'. These findings may serve as researchers' references to find collaborative authors, relevant journals, cooperative countries/institutions, and hot topics related to dMRI and reward research.

What are the contrasting types of proactivity that manifest at work? A systematic literature review, content analysis, and future directions

PurposeThis study summarizes widely dispersed literature on proactivity at work in its many different forms and highlights contrasts between the various themes in detail.Design/methodology/approachThis study is based on systematic literature review of proactivity at work using 1708 peer reviewed articles published between 1969 and 2021 using R and Vos viewer software; a content analysis of the 100 most cited articles in proactivity research; and synthesis and integrative literature review to develop future research directions.FindingsThis study uncovers many interesting facets of knowledge, including proactivity-related themes that have emerged over 50 years of research, time-related publication trends, top journals, top authors, and the most commonly used keywords. The content analysis of the 100 most-cited articles revealed findings such as the role of each proactivity theme impacting academic discussions to date. For example, quantitative research appears to be more prevalent among the most cited articles compared to different types of qualitative research. Finally, future research directions building on our findings are provided.Originality/valueTo our knowledge, this work is the first attempt to contrast different types of proactivity at work as opposed to extant literature, which is either driven by the proactivity phenomenon or focuses on similarities rather than on contrasting aspects of the various forms of proactivity at work. Hence, the significance of this research extends beyond theoretical considerations and incorporates valuable practical elements.

Gender and Geographical Representation on Editorial Board Members of Medical Informatics Journals.

Previous work has suggested that gender and geographical distribution (affiliation) of Editors-in-Chief (EiC) and Editorial Board (EB) members are inequitable in representation of scientific communities, and could benefit from increasing diversity of representation. Specifically, previous studies suggest that male and ethnically white (or non-minoritized groups) are overrepresented. Such differences in representation may potentially influence the scientific and scholarly record. This paper aims to build on pre-existing literature by examining the diversity of representation among EiCs and EB members in the top (Q1) journals in the "Medicine-Health Informatics" category (ranked by SCImago Journal and Country Rank, or SJR) in terms of gender as assessed by genderize.io) and geographical distribution of affiliations. Preliminary findings are consistent with those of previous work on the topic: only 25% (8/32) of the EiCs in the selected journals are female, while females only represent 32.7% (426/1303) of the EB members across journals. Furthermore, the US is highly represented in EBs, with more than half of the members, i.e., 52.2% (698/1337), being US-affiliated. Present results suggest the need for an intentional approach to diversifying representation on editorial boards of medical informatics journals. Such intention can be seen as part of a call to action from important stakeholders, including medical informatics leaders and programs, journal management and publishers, and the medical informatics and scientific community more generally.

Publications on COVID-19 and artificial intelligence: trends and lessons

Purpose: This study investigates shifts in scientific research focus, particularly the decline in COVID-19-related research and the rapid growth of artificial intelligence (AI) publications.Methods: We analyzed publication data from the Web of Science, comparing yearly publication counts for COVID-19 and AI research. The study also tracked changes in the impact factors of leading journals like <i>Science</i> and <i>Nature</i>, alongside those of top AI journals over the past decade. Additionally, we reviewed the top 10 most cited articles in 2021 from <i>Science</i> and <i>Nature</i> and the most influential AI publications from the past five years according to Google Scholar. The impact trends of the top 100 AI journals in computer science were also explored.Results: The analysis reveals a noticeable decline in COVID-19 related publications as the pandemic urgency diminishes, contrasted with the continued rapid growth of AI research. Impact factors for prestigious journals have shifted, with AI journals increasingly dominating the academic landscape. The review of top-cited articles further emphasizes these trends.Conclusion: Our findings indicate a significant shift in research priorities, with AI emerging as a dominant field poised to address future challenges, reflecting the evolving focus of the scientific community.

Application of artificial intelligence in coal mine ultra-deep roadway engineering—a review

The deep integration of computer field and coal mining field is the only way to coal mine intellectualization. A variety of artificial intelligence tools have been applied in open-pit and shallow coal mines. However, with the geometric increase of coal demand, the contradiction between supply and demand is becoming more and more serious, and the exploitation of resources from shallow layer (> 600 m) has become an inevitable trend. Well then, as a new engineering scene, the harsh conditions of “three high and one disturbance” seriously threaten the safety of personnel. The superposition of complex mining environment makes the number of input factors increase sharply, which leads to the application of artificial intelligence in deep coal mine roadway engineering. The guidance is not mature, the construction of various databases is missing, and there are still some problems in universality and applicability. To this end, this paper starts with the introduction of the operating characteristics of various artificial intelligence tools, and conducts a comprehensive study of the relevant high-level articles published in top journals. It systematically sorts out the research progress that has successfully solved the five directions of rock mechanics strength, surrounding rock stability, rock-burst, roof fall risks and micro-seismic events. While objectively evaluating the comprehensive performance of different tools, it also expounds its own views on key research and results. Literature review shows that whether as a development tool or a comparative model, the application of ANN in the field of coal mining is more than 98%, and it performs extremely well in the direction of surrounding rock stability and roof fall risk, with an accuracy rate of more than 90%. As the most mature direction of AI tools application, rock mechanical strength has experienced the development process from “SVM → ANN → DL → XGBoost → RF”. The dataset is from small samples (< 100) to big samples (> 1000), and the R2 of tree-based models can be stabilized at more than 95%. The research on rock-burst prediction mainly focuses on field factors and micro-seismic monitoring data. Whether it is a small sample or a large-scale data model, the accuracy of BN remains above 85%. The prediction and evaluation of micro-seismic events is a new direction in recent years. The image processing and application of CNN is extremely important. The signal recognition and classification accounts for more than 90%, and the research potential of source location needs to be further explored. In general, the nature of the rock itself is the first choice for almost all influencing factors. At the same time, the update iteration of monitoring methods (micro-seismic, ground sound, separation, deformation, etc.) expands the development of the database, making it possible to obtain the data due to threat to life and cost of equipment, which is very difficult to obtain before. In the process of parameter selection at the input end, the method of combining lithology conditions, geological environment and monitoring data will gradually become the first choice for research. Finally, in the follow-up work collation and on-the-spot investigation, it mainly focuses on the existing problems of deep coal mines, explores the application potential of artificial intelligence in deep coal mine roadway engineering, puts forward the possible research focus and challenging problems in the future, and gives its own opinions.

Integrated management systems: a content and bibliometric analysis

PurposeSignificant interest in Integrated Management Systems (IMS), as a key area within ISO-related Management System Standards (MSS) literature, has been evident from both academia and industry over the past three decades. This study aims to map the evolution and current state of IMS research and propose possible directions for future studies.Design/methodology/approachA comprehensive content and bibliometric analysis of 846 documents from the Scopus database across the period 1995 to 2023 was conducted. This included performance analysis to track publication trends and identify key contributors, and content analysis to specify dominant research methodologies and the MSS most commonly integrated. Furthermore, science mapping techniques—such as co-authorship networks, keyword co-occurrence analysis, and bibliographic coupling—were utilized to outline the collaborative networks and the conceptual and intellectual structure of the field.FindingsThe study identifies three principal IMS research themes: the practical implementation of IMS, their role in promoting sustainability and social responsibility, and their impact on continuous performance improvement. It also highlights the field’s evolution and key research constituents—including influential works, prolific authors, leading academic institutions and countries, and top publishing journals. It further reveals that IMS research exhibits strong collaboration across authors and countries, and a rich methodological plurality, notably with a marked increase in empirical surveys in recent years. Additionally, it identifies the most frequently referenced MSS for integration, prominently featuring ISO 9001, ISO 14001, and ISO 45001/OHSAS 18001.Originality/valueThis study is original in its application of a dual analytical approach—bibliometric and content analysis—to provide a holistic overview of IMS research. It offers new insights into the integration of diverse MSS and proposes several promising paths for future research. Among the most prominent are standardizing IMS fundamental specifications, conducting more empirical research with advanced methods to evaluate the effects of MSS integration, providing practical support for organizations in IMS implementation through tailored methodologies and tools, and exploring the potential of Industry 4.0 and 5.0 technologies to advance IMS practices.

Application of the Relative Citation Ratio to Assess Common Characteristics of the Highest Impact Articles in Reconstructive Microsurgery

Background: The purpose of this review is to characterize themes among the five plastic and reconstructive microsurgery articles achieving the highest relative citation ratios (RCR) published in the last 20 years in the top journals. In doing so, researchers may be better informed on how to propose salient research questions to impact the field and understand future directions in plastic surgery. Methods: A cross-sectional study was conducted with articles published in the top three journals based on the Impact Factor: Plastic and Reconstructive Surgery, Journal of Reconstructive Microsurgery, and Annals of Plastic Surgery. A search strategy with controlled vocabulary and keywords was conducted in PubMed to extract all reconstructive microsurgery (RM) articles published between 2002 and 2020. A two-stage screening process to include only RM studies was performed, with a third reviewer moderated discordances. Articles’ RCR data was extracted from NIH iCite. The top five articles with the highest RCR were selected for analysis. Results: We identified three features reflecting educational and clinical trends within RM that might be representative of super-performance in plastic surgery journals. These include: 1) Relevance to high yield techniques in RM such as tissue flap procurement, indications, and outcomes 2) Identification of gaps in current knowledge of these topics 3) Use of media and algorithms to provide clear recommendations. Conclusion: Researchers hoping to have an impactful contribution should pose research questions that address these key themes. The RCR index is a valuable tool to appreciate performance within RM literature and clinical trends within the field.