Characteristics of Publications on Occupational Stress: Contributions and Trends.
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ABSTRACT:
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8239141 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective: Electrical impedance tomography (EIT) is a technique to measure electrical properties of tissue. With the progress of modern integrated circuits and microchips, EIT instrumentation becomes an active research area to improve all aspects of device performance. Plenty of studies on EIT hardware have been presented in prestigious journals. This study explores publications on EIT hardware to identify the developing hotspots and trends. Method: Publications covering EIT hardware on the Web of Science Core Collection (WoSCC) database from 1989 to 2021 were collected for bibliometric analysis. CiteSpace and VOS viewer were used to study the characteristics of the publications. Main results: A total of 592 publications were analyzed, showing that the number of annual publications steadily increased. China, England, and South Korea were the most prolific countries on EIT hardware publications with productive native institutions and authors. Research topics spread out in "bio-electrical impedance imaging", "hardware optimization", "algorithms" and "clinical applications" (e.g., tissue, lung, brain, and oncology). Hardware research in "pulmonary" and "hemodynamic" applications focused on monitoring and were represented by silhouette recognition and dynamic imaging while research in "tumor and tissue" and "brain" applications focused on diagnosis and were represented by optimization of precision. Electrode development was a research focus through the years. Imaging precision and bioavailability of hardware optimization may be the future trend. Conclusion: Overall, system performance, particularly in the areas of system bandwidth and precision in applications may be the future directions of hardware research.
| S-EPMC9608147 | biostudies-literature
Project description:BackgroundThis study aims to identify the most frequently cited articles published in the Journal of Arthroplasty (JOA) and to analyze the trends in the content and contributors of the literature within the journal.MethodsThe 100 most cited articles published in the JOA were accessed using the Scopus database. The number of citations, year of publication, level of evidence (LOE), article type, country of origin, and contributing institution were each recorded for each article.ResultsThe United States (63%) was the most prolific publishing nation. The 1990s (30%) and 2000s (47%) were the most productive decades. The most common article category was clinical outcomes (33%), followed by technical note (16%) and biomechanics (14%). The plurality of the top 100 articles were well-designed case-control or cohort studies of LOE II (46%) followed by LOE V (32%) and LOE I (11%).ConclusionsUsing citation analysis, the most influential articles in the JOA were comprehensively and objectively analyzed. The most popular fields of research involved clinical outcomes (33%) and technical note (16%), both of which increase an article's likelihood of being highly cited. Knowledge of the most influential articles in the JOA allows for appreciation of current and potential future areas of literature regarding diagnosis, management, and outcome of a patient undergoing arthroplasty.
| S-EPMC9307491 | biostudies-literature
Project description:PurposeArthroscopy is an established sub-speciality in orthopaedics. With advancing technology, instrumentation and implants, this sub-speciality has seen an explosion of knowledge and techniques since its inception. The indications for arthroscopic management are increasing and, hence, the number of publications on this topic. There has been no study looking into the bibliometrics of all publications within this speciality. The purpose of this study was to look into the trends of published articles on arthroscopy from PubMed and Scopus including studying their citation numbers.Materials and methodsWe set out to look into the number of publications from the earliest up to 2019 and their trends and citation numbers in PubMed and Scopus. We also performed a VOS viewer analysis of MeSH terms and titles of publications to look at research trends over time.ResultsThere were 41,149 articles published on PubMed since 1955 and 50,373 articles on Scopus since 1939. The total number of citations were 912,630 for 38,338 cited articles. With 2864 publications in 2019, there was a more than four-fold increase from the number published in the year 2000. The knee joint was the most frequently published joint with an increasing trend in hip arthroscopy. Cohort studies were the most common with 13,180 articles followed by Reviews with 5746 articles. The top 10 authors, universities and journals were listed along with citation numbers. We analysed the trends of publications for each joint and compared them. Yearly citations have progressively increased to reach a maximum of 45,407 in 2007. Arthroscopy was the most published and cited journal on this topic. The Journal of Bone and Joint Surgery (JBJS) (Am) had the most citations per article. The USA and Hospital for Special Surgery, New York were the most published country and university, respectively.ConclusionsThere is a healthy growth of publications on the subject of arthroscopy with a steep increase in the number of publications and citations in recent years. VOS Viewer analysis showed an evolution of research and practice in the field of arthroscopy. Recommendations were made for databases and search engines to improve on the search and analysis of such studies in the future.Level of evidence4.
| S-EPMC8082866 | biostudies-literature
Project description:Study objectiveTo determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields.Design/settingTo evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed.ResultsFindings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on "machine learning" (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to "artificial intelligence" (n = 4535), "natural language processing" (n = 2608), and "deep learning" (n = 4459).ConclusionTrends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.
| S-EPMC10978330 | biostudies-literature
Project description:Purpose: The aim of this study was to comparatively analyze evolving trends in physiotherapy (PT) research publications (excluding case reports and epidemiological and qualitative studies) between 1995 and 2015, inclusively in terms of research design, funding support, age groups, and health conditions. Method: This was an observational study using PubMed-indexed data. Combinations of medical subject headings identified yearly research publications for PT and comparator fields: human-based health and physical rehabilitation. Yearly publications data were extracted, relative percentages were computed, and linear or exponential regressions examined the yearly growth in the proportion of research publications over these 2 decades. Results: As a percentage of human-based health research publications, PT research publications grew exponentially: from 0.54% in 1995 to 2.37% in 2015 (r² = 0.97; p < 0.01). As a percentage of physical rehabilitation research publications, PT research grew from 38.2% in 1995 to 58.7% in 2015 (r² = 0.89; p < 0.01). Randomized controlled trials (RCTs) resulted in the majority of PT research publications (from 45.1% in 1995 to 59.4% in 2015; r² = 0.79; p < 0.01). Rates of declared funding increased (from 29.7% in 1995 to 57% in 2015; r² = 0.83; p < 0.01), but the comparator fields had similar growth. The percentage of PT research publications remained stable for most health conditions and age groups, decreased for those aged 0-18 years (p = 0.012) and for cardiovascular and pulmonary conditions (both p < 0.01), and increased for neoplasms (p < 0.01). Conclusions: PT research publications have become more prevalent among health and physical rehabilitation research publications; the majority of publications report on RCTs.
| S-EPMC7238935 | biostudies-literature
Project description:Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.
| S-EPMC8522017 | biostudies-literature
Project description:Declining biodiversity and ecosystem functions put many of nature's contributions to people at risk. We review and synthesize the scientific literature to assess 50-y global trends across a broad range of nature's contributions. We distinguish among trends in potential and realized contributions of nature, as well as environmental conditions and the impacts of changes in nature on human quality of life. We find declining trends in the potential for nature to contribute in the majority of material, nonmaterial, and regulating contributions assessed. However, while the realized production of regulating contributions has decreased, realized production of agricultural and many material commodities has increased. Environmental declines negatively affect quality of life, but social adaptation and the availability of substitutes partially offset this decline for some of nature's contributions. Adaptation and substitutes, however, are often imperfect and come at some cost. For many of the contributions of nature, we find differing trends across different countries and regions, income classes, and ethnic and social groups, reinforcing the argument for more consistent and equitable measurement.
| S-EPMC7768808 | biostudies-literature
Project description:BackgroundClinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs).MethodsExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study.ResultsWe evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (second stage) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers.ConclusionsOur experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).
| S-EPMC2954855 | biostudies-literature
Project description:ObjectivesThe growing rate of retraction of scientific publications has attracted much attention within the academic community, but there is little knowledge about the nature of such retractions in schizophrenia-related research. This study aimed to analyze the characteristics of retractions of schizophrenia-related publications.Materials and methodsThe Web of Science was searched for eligible studies. A bibliometric analysis was conducted to describe the characteristics of the retractions using R software and Excel 2019. Content analysis was conducted to examine the essential components of retraction notices.ResultsA total of 36 retracted publications with 415 citations were identified from 1997 to 2021, of which, 83.3% occurred in the last decade. The overall retraction rate was 0.19%, with most of them (29; 80.56%) from the United Kingdom. The retractions were published in 33 journals, and the 2020 IFs ranged between 0.17 and 49.96 (Median = 3.93). The retractions involved 21 research areas, particularly in Psychiatry (19; 52.78%), Neurosciences and Neurology (10; 27.78%), and Psychology (7; 19.44%). Data issues (17; 42.22%), administrative errors of the publishers (5; 13.89%), and study design (4; 11.11%) were the top three reasons for retractions.ConclusionThis study provides an insight into retractions of schizophrenia-related publications. Institutional governance should be further strengthened to improve the scrutiny of publications, prevent continuing citations, and erroneous propagation after retraction.
| S-EPMC9376617 | biostudies-literature
Project description:The World Health Organization assigns international nonproprietary names (INN), also known as common names, to compounds upon request from drug developers. Structures of INNs are publicly available and represent a source, albeit underused, to understand trends in drug research and development. Here, we explain how a common drug name is composed and analyze chemical entities from 2000 to 2021. In the analysis, we describe some changes that intertwine chemical structure, newer therapeutic targets (e.g., kinases), including a significant increase in the use of fluorine and of heterocycles, and some other evolutionary modifications, such as the progressive increase in molecular weight. Alongside these, small signs of change can be spotted, such as the rise in spirocyclic scaffolds and small rings and the emergence of unconventional structural moieties that might forecast the future to come.
| S-EPMC8154580 | biostudies-literature