Manual versus machine: How accurately does the Medical Text Indexer (MTI) classify different document types into disease areas?

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the U.S. National Library of Medicine (NLM) for classifying journal articles. It is increasingly used by researchers studying medical innovation to classify text into disease areas and other categories. Although this process was once manual, human indexers are now assisted by algorithms that automate some of the indexing process. NLM has made one of their algorithms, the Medical Text Indexer (MTI), available to researchers. MTI can be used to easily assign MeSH descriptors to arbitrary text, including from document types other than publications. However, the reliability of extending MTI to other document types has not been studied directly. To assess this, we collected text from grants, patents, and drug indications, and compared MTI's classification to expert manual classification of the same documents. We examined MTI's recall (how often correct terms were identified) and found that MTI identified 78% of expert-classified MeSH descriptors for grants, 78% for patents, and 86% for drug indications. This high recall could be driven merely by excess suggestions (at an extreme, all diseases being assigned to a piece of text); therefore, we also examined precision (how often identified terms were correct) and found that most MTI outputs were also identified by expert manual classification: precision was 53% for grant text, 73% for patent text, and 64% for drug indications. Additionally, we found that recall and precision could be improved by (i) utilizing ranking scores provided by MTI, (ii) excluding long documents, and (iii) aggregating to higher MeSH categories. For simply detecting the presence of any disease, MTI showed > 94% recall and > 87% precision. Our overall assessment is that MTI is a potentially useful tool for researchers wishing to classify texts from a variety of sources into disease areas.

Mapping the concept of health care integration: A lexicographic analysis of scientific literature.

BACKGROUND: Systems fragmentation is a major challenge for an efficient organization, integration being a potential solution also proposed in health care field, including pharmacy as a player. However, the use of different terms and definitions in the literature hinders the comparison of different integration initiatives. OBJECTIVE: To identify and map the terms used in scientific literature regarding integration in health care and to characterize each emerging topic. METHODS: A lexicographic analysis of the integration of healthcare systems literature indexed in PubMed was conducted. Ten different systematic searches, four using only Medical Subject Headings (MeSH) and six using text words, were conducted in March 2023. Journal scattering was analyzed following Bradford's distribution using the Leimkuhler model. An overall text corpus was created with titles and abstracts of all the records retrieved. The corpus was lemmatized, and the most used bigrams were tokenized as single strings. To perform a topic modeling, the lemmatized corpus text was analyzed using IRaMuTeQ, producing descending hierarchic classification and a correspondence analysis. The 50 words with higher chi-square statistics in each class were considered as representative of the class. RESULTS: A total of 42,479 articles published from 1943 to 2023 in 4469 different journals were retrieved. The MeSH "Delivery of Health Care, Integrated", created in the 1996 MeSH update, was the most productive retrieving 33.7 % of the total articles but also retrieving 22.6 % of articles not retrieved in any other search. The text word "Integration" appeared in 15,357 (36.2 %) records. The lexicographic analysis resulted in 7 classes, named as: Evidence and implementation, Quantitative research, Professional education, Qualitative research, Governance and leadership, Clinical research, and Financial resources. Association between the classes and the searches or the text-words used ranged from moderate to weak demonstrating the lack of a standard pattern of use of terms in literature regarding healthcare integration. CONCLUSIONS: The term "integration" and the MeSH "Delivery of Health Care, Integrated" are the most used to represent the concept of integration in healthcare and should be the preferred terms in the literature.

General practice-related MeSH terms in main journals: a bibliometric analysis from 2011 to 2021.

BACKGROUND: There are various Medical Subject Headings (MeSH) terms used to index general practice research, without consistency. AIM: To understand how general practice-related research is indexed in the main general practice journals between 2011 and 2021, and to analyse the factors that influenced the choice of the general practice-related MeSH. DESIGN AND SETTING: This was a quantitative bibliometric study conducted on MEDLINE. METHOD: MeSH were selected according to the international definition of General Practice/Family Medicine: 'General Practice', 'Primary Health Care', 'Family Practice', 'General Practitioners', 'Physicians, Primary Care', and 'Physicians, Family'. Their use was studied from 2011 to 2021 on MEDLINE, reviewing the 20 general practice journals with the highest impact factors. A descriptive and analytical approach was used; the association of the country, journal, and year with the choice of general practice-related MeSH terms was analysed. RESULTS: A total of 8514 of 150 286 articles (5.7%) were using one of the general practice-related MeSH terms. The most used were 'Primary Health Care' (4648/9984, 46.6%) and 'General Practice' (2841/9984, 28.5%). A total of 80.0% (6172/7723) of the articles were related to the UK or US and 71.0% (6055/8514) of the articles came from four journals (BJGP, BMJ, Journal of General Internal Medicine, and Annals of Family Medicine). Two main country clusters emerged from the use of general practice-related MeSH: a British cluster mainly using 'General Practice' and an American cluster using 'Primary Health Care'. The journals also mainly differed in their used of these two MeSH terms. CONCLUSION: Important variations in the indexation of general practice research were found. Researchers should consider combining 'Primary Health Care' and 'General Practice' in their PubMed searches to access all the general practice research, regardless of their country of origin.

The MeSH heading "Call Center" is due for an update: why we recommend the more precise heading "Emergency Medical Communication Center".

Call centers can be found in various industries. However as a Medical Subject Heading (MeSH) the term "Call centers" does not reflect the critical purpose of handling emergency calls. We recommend "emergency medical communication center(s)", as this provides clarity and precision regarding the primary function and purpose of the center.

Automatic Annotation of PubMed Articles with MeSH Qualifiers.

The Medical Subject Headings (MeSH) is a comprehensive indexing vocabulary used to label millions of books and articles on PubMed. The MeSH annotation of a document consists of one or more descriptors, the main headings, and of qualifiers, subheadings specific to a descriptor. Currently, there are more than 34 million documents on PubMed, which are manually tagged with MeSH terms. In this paper, we describe a machine-learning procedure that, given a document and its MeSH descriptors, predicts the respective qualifiers. In our experiment, we restricted the dataset to documents with the Heart Transplantation descriptor and we only used the PubMed abstracts. We trained binary classifiers to predict qualifiers of this descriptor using logistic regression with a tfidf vectorizer and a fine-tuned DistilBERT model. We carried out a small-scale evaluation of our models with the Mortality qualifier on a test set consisting of 30 articles (15 positives and 15 negatives). This test set was then manually re-annotated by a cardiac surgeon, expert in thoracic transplantation. On this re-annotated test set, we obtained macroaveraged F1 scores of 0.81 for the logistic regression model and of 0.85 for the DistilBERT model. Both scores are higher than the macroaveraged F1 score of 0.76 from the initial PubMed manual annotation. Our procedure would be easily extensible to all the MeSH descriptors with sufficient training data and, we believe, would enable human annotators to complete the indexing work more easily.Clinical Relevance-Selecting relevant articles is important for clinicians and researchers, but also often a challenge, especially in complex subspecialties such as heart transplantation. In this study, a machine-learning model outperformed PubMed's manual annotation, which is promising for improved quality in information retrieval.

Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis.

BACKGROUND: The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis. METHODS: A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms). RESULTS: Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%. CONCLUSIONS: No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.

A thematic study of creativity research in Spain within the social sciences and psychology / Un estudio temático de la investigación en creatividad en España en las ciencias sociales y psicología

La creatividad se está convirtiendo en una habilidad necesaria en un mundo donde los robots superan cada vez más a las personas en las rutinas diarias. Para desarrollar eficientemente el campo de investigación de la creatividad, los académicos necesitan saber dónde están. Este artículo utiliza un enfoque bibliométrico para estudiar temas y características de la investigación en creatividad en España. Los resultados indican que la producción ha ido creciendo durante las últimas décadas. En comparación con la psicología, la creatividad en las ciencias sociales parece ser un área poco citada, local y endogámica. Para las ciencias sociales, los temas motores en la última década fueron a) la creatividad en niños y estudiantes en un entorno educativo, b) la innovación y creación de conocimiento en un entorno laboral, y c) las ciudades creativas. Los temas motores en psicología han sido a) las características individuales para generar conocimientos (por ejemplo, habilidades, improvisación, funciones ejecutivas) y b) la inteligencia emocional. Sugerimos algunos temas para futuras investigaciones, como la colaboración creativa en un entorno virtual, la co-creación de valor, y cómo las máquinas pueden ayudar a los humanos a impulsar su creatividad.(AU)

Creativity is becoming one necessary human skill in a world where robots increasingly outperform people in daily routines. In order to efficiently develop creativity as a research field, scholars need to know where they are.We employed a bibliometric approach to study themes and characteristics of creativity research in Spain.The results indicated that publication production in the field has been growing during the last dec-ades. Compared to psychology, creativity in the social sciences seemed to be an undercited, local,and endogamic area. For social sciences, motor themes in the last decade were a) creativity in children and students in the educational environment, b) innovation and knowledge creation in a work-ing environment, and c) cities and creativity. The motor themes in psy-chology were a) individual characteristics for generating insights (e.g., skills, improvisation, executive functions) and b) emotional intelligence. We sug-gest some themes for future research, such as creative collaboration in vir-tual environments, value co-creation, and how machines can help humans boost their creativity.(AU)

Historical Review of Acute Pancreatitis Research Over the Last 80 Years.

OBJECTIVES: Research on acute pancreatitis (AP) has been ongoing for a long time. It is necessary to summarize and investigate the history of AP research. METHODS: Publications related to AP research were retrieved from PubMed. Medical Subject Headings (MeSH) terms, countries, journals, and publication dates were analyzed. Co-occurrence analysis was conducted to illustrate the holistic trend in AP research. A dynamic bar graph, heat maps, and line charts were created to illustrate change trends of MeSH terms. RESULTS: In total, 28,222 publications with 8558 MeSH terms were retrieved from 1941 to 2020. Among these, 16,575 publications with 7228 MeSH terms were from 2001 to 2020. The top 10 MeSH terms showed a considerable change from 1941 to 1970 but remained stable since the 1970s. Four clusters obtained from the co-occurrence analysis were "experiments on animals," "diagnosis and treatment," "prognosis and expectation," and "protein and enzyme." From 1941 to 2020, 33 MeSH terms with increasing trends (MH-I) and 15 MeSH terms with decreasing trends (MH-D) were selected to create a heat map (every decade). Meanwhile, 16 MH-I and 41 MH-D were selected to create the heat map from 2001 to 2020 (every 2 years). CONCLUSION: Over the past 80 years, the pathogenesis, treatment, risk management, and experimental model were the main research highlights. Optimal supportive management, minimally invasive treatment, and prediction of prognosis are subjects of interest for clinical practitioners; signal transduction to identify a target for precise treatment is the focus of experimental research in AP.

Analysis and implementation of the DynDiff tool when comparing versions of ontology.

BACKGROUND: Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to understand and manage these changes and their impact on depending systems well in order to handle the growing volume of data annotated with ontologies and the limited documentation describing the changes. METHODS: We present a method to detect and characterize the changes occurring between different versions of an ontology together with an ontology of changes entitled DynDiffOnto, designed according to Semantic Web best practices and FAIR principles. We further describe the implementation of the method and the evaluation of the tool with different ontologies from the biomedical domain (i.e. ICD9-CM, MeSH, NCIt, SNOMEDCT, GO, IOBC and CIDO), showing its performance in terms of time execution and capacity to classify ontological changes, compared with other state-of-the-art approaches. RESULTS: The experiments show a top-level performance of DynDiff for large ontologies and a good performance for smaller ones, with respect to execution time and capability to identify complex changes. In this paper, we further highlight the impact of ontology matchers on the diff computation and the possibility to parameterize the matcher in DynDiff, enabling the possibility of benefits from state-of-the-art matchers. CONCLUSION: DynDiff is an efficient tool to compute differences between ontology versions and classify these differences according to DynDiffOnto concepts. This work also contributes to a better understanding of ontological changes through DynDiffOnto, which was designed to express the semantics of the changes between versions of an ontology and can be used to document the evolution of an ontology.

Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966-2020).

Background: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH). Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots. Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected. Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.

Automated indexing using NLM's Medical Text Indexer (MTI) compared to human indexing in Medline: a pilot study.

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches.

The 10 top-cited authors who published papers in journal medicine since 2000 using the betweenness centrality to identify unique names: Bibliometric analysis.

BACKGROUND: Numerous studies have explored the most productive and influential authors in a specific field. However, 2 challenges arise when conducting such research. First, some authors may have identical names in the study data, and second, the contributions of coauthors may vary in the article by line, requiring consideration. Failure to address these issues may result in biased research findings. Our objective was to illustrate how the author-weighted scheme (AWS) and betweenness centrality (BC) can be employed to identify the 10 most frequently cited authors in a particular journal and analyze their research themes. METHODS: We collected 24,058 abstracts from the PubMed library between 2000 and 2020 using the keyword "Medicine [Journal]." Author names, countries/regions, and medical subject headings (MeSH terms) were collected. The AWS to identify the top 10 authors with a higher x-index was applied. To address the issue of authors with identical names affiliated with different research institutes, we utilized the BC method. Social network analysis (SNA) was conducted, and 10 major clusters were identified to highlight authors with a higher x-index within the corresponding clusters. We utilized SNA to analyze the MeSH terms from articles of the 10 top-cited authors to identify their research themes. RESULTS: Our findings revealed the following: within the top 10 cited authors, 2 authors from China shared identical names with Jing Li and Tao-Wang; JA Winkelstein from Maryland (US) had the highest x-index (15.58); Chia-Hung Kao from Taiwan was the most prolific author, having published 115 articles in Medicine since 2003; and the 3 primary research themes, namely, complications, etiology, and epidemiology, were identified using MeSH terms from the 10 most frequently cited authors. CONCLUSIONS: Using AWS and BC, we identified the top 10 most cited authors. The research methods we utilized in this study (BC and AWS) have the potential to be applied to other bibliometric analyses in the future.

Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices.

OBJECTIVE: Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies. MATERIALS AND METHODS: To retrieve articles from PubMed that addressed patient/consumer engagement with wearables, we developed a search strategy of textwords and Medical Subject Headings (MeSH). To refine our methodology, we used a random sample of 200 articles from 2016 to 2018. A descriptive analysis of articles (N = 2522) from 2019 identified 308 (12.2%) CHI-related articles, for which we characterized their assigned terminology. We visualized the 100 most frequent terms assigned to the articles from MeSH, author keywords, CINAHL, and Engineering Databases (Compendex and Inspec together). We assessed the overlap of CHI terms among sources and evaluated terms related to consumer engagement. RESULTS: The 308 articles were published in 181 journals, more in health journals (82%) than informatics (11%). Only 44% were indexed with the MeSH term "wearable electronic devices." Author keywords were common (91%) but rarely represented consumer engagement with device data, eg, self-monitoring (n = 12, 0.7%) or self-management (n = 9, 0.5%). Only 10 articles (3%) had terminology from all sources (authors, PubMed, CINAHL, Compendex, and Inspec). DISCUSSION: Our main finding was that consumer engagement was not well represented in health and engineering database thesauri. CONCLUSIONS: Authors of CHI studies should indicate consumer/patient engagement and the specific technology investigated in titles, abstracts, and author keywords to facilitate discovery by readers and expand vocabularies and indexing.

Developing a Comprehensive Search Strategy for the Systematic Review of Clinical Decision Support Systems for Nursing Practice.

The search strategy of a literature review is of utmost importance as it impacts the validity of its findings. In order to build the best query to guide the literature search on clinical decision support systems applied to nursing clinical practice, we developed an iterative process capitalizing on previous systematic reviews published on similar topics. Three reviews were analyzed relatively to their detection performance. Errors in the choice of keywords and terms used in title and abstract (missing MeSH terms, failure to use common terms), may make relevant articles invisible.

Diferença entre os vocábulos bucal e oral na terminologia odontológica e seus significados na língua portuguesa / Difference between oral and oral vocables in dental terminology and their meanings in portuguese language

O objetivo do presente trabalho foi discutir sobre a diferença entre os termos bucal e oral na Odontologia, tendo como respaldo a Língua Portuguesa. A metodologia bibliográfica buscou se ancorar em teóricos da linguagem e da Odontologia para investigar a diferença entre tais palavras. Após leitura de textos que versaram sobre essas vertentes, observamos que a principal diferença residiu na origem das palavras bucca e os, originárias do latim clássico e vulgar, respectivamente, com significados distintos. Todavia, ao migrarem para o português, os falantes escolheram o termo bucca em detrimento de os, o qual ainda hoje é usado ao lado de oral, com sentidos semelhantes. Notamos, ainda, que para os profissionais da saúde seria importante padronizar a terminologia, pois facilitaria a compreensão desses termos para pacientes e profissionais de outras áreas, tais como os tradutores; por outro lado, ficou nítido que, em alguns momentos, a unificação terminológica seria mais difícil, pois os contextos de uso teriam que ser mudados. Por fim, destacamos que estudar estes vocábulos no contexto da Odontologia é importante para que tanto pacientes quanto os profissionais de saúde, ou de áreas similares conheçam a peculiar diferença(AU)

The objective of the present work was to discuss the difference between the terms oral and oral in Dentistry, based on the Portuguese language. The bibliographic methodology sought to anchor in language and dentistry theorists to investigate the difference between such words. After reading texts that dealt with these aspects, we observed that the main difference resided in the origin of the word bucca and os, originating from classical and vulgar Latin, respectively, with different meanings. However, when migrating to Portuguese, the speakers chose the term bucca over os, which is still used alongside oral, with similar meanings. We also noted that for health professionals it would be important to standardize the terminology, as it would facilitate the understanding of these terms for patients and professionals from other areas, such as translators; on the other hand, it was clear that at times, terminological unification would be more difficult, as the contexts of use would have to be changed. Finally, we emphasize that studying these words in the context of Dentistry is important so that both patients and health professionals, or from similar areas, know the peculiar difference(AU)

WeakMeSH: Leveraging provenance information for weakly supervised classification of biomedical articles with emerging MeSH descriptors.

Medical Subject Headings (MeSH) is a hierarchically structured thesaurus created by the National Library of Medicine of USA. Each year the vocabulary gets revised, bringing forth different types of changes. Those of particular interest are the ones that introduce new descriptors in the vocabulary either brand new or those who come up as a product of a complex change. These new descriptors often lack ground truth articles and rendering learning models that require supervision not applicable. Furthermore, this problem is characterized by its multi label nature and the fine-grained character of the descriptors that play the role of classes, requiring expert supervision and a lot of human resources. In this work, we alleviate these issues through retrieving insights from provenance information about those descriptors present in MeSH to create a weakly labeled train set for them. At the same time, we make use of a similarity mechanism to further filter the weak labels obtained through the descriptor information mentioned earlier. Our method, called WeakMeSH, was applied on a large-scale subset of the BioASQ 2018 data set consisting of 900 thousand biomedical articles. The performance of our method was evaluated on BioASQ 2020 against several other approaches that had given competitive results in similar problems in the past, or apply alternative transformations against the proposed one, as well as some variants that showcase the importance of each different component of our proposed approach. Finally, an analysis was performed on the different MeSH descriptors each year to assess the applicability of our method on the thesaurus.

Risco de exacerbação de asma em adolescentes usuários de dispositivos eletrônicos de liberação de nicotina: uma revisão sistemática e metanálise / Risk of asthma exacerbation in adolescent users of electronic nicotine delivery systems: a systematic review and meta-analysis

Este trabalho tem como objetivo investigar a associação entreo o uso dos cigarros eletrônicos e doenças pulmonares em adolescentes. Foi realizada uma revisão sistemática na base de dados PubMed. Os termos Mesh incluídos na busca foram "Electronic Nicotine Delivery Systems" e "Lung Diseases" e sinônimos no título e abstract, com o filtro de idade "child: birth - 18 years", para buscar artigos relacionados ao uso de cigarros eletrônicos e doenças pulmonares em adolescentes. Os critérios de elegibilidade consistiram em: usuários adolescentes, exposição ao cigarro eletrônico e doença pulmonar como desfecho. Os artigos foram selecionados por uma revisão pareada de maneira independente, primeiramente com a leitura dos títulos e resumos, seguida da leitura integral dos artigos selecionados, os quais foram analisados pela ferramenta New Castle-Ottawa quanto sua qualidade, e receberam entre 5 e 7 estrelas. Os dados encontrados foram extraídos para a realização da metanálise. Inicialmente foram encontrados 61 artigos, sendo seis considerados elegíveis, todos transversais e com aplicação de questionários. Na metanálise foi encontrada uma associação significativa entre o uso de cigarro eletrônico e exacerbação de asma (OR ajustado 1,44; IC 95% 1,17­1,76). Não foram encontrados estudos que avaliassem a associação do cigarro eletrônico e outras doenças pulmonares, incluindo EVALI (E-cigarette or Vaping product use-Associated Lung Injury), em adolescentes. Na metanálise foi encontrada uma associação significativa entre exacerbações de asma e uso de cigarros eletrônicos em adolescentes com asma crônica e nos previamente hígidos.

This study aims to investigate the association between electronic cigarette use and lung disease in adolescents. A systematic review was conducted in PubMed. We used the MeSH terms "Electronic Nicotine Delivery Systems" and "Lung Diseases" as well as synonyms in the title and abstract, with the age filter "child: birth - 18 years" to search for articles related to electronic cigarette use and lung disease in adolescents. The eligibility criteria consisted of adolescent users and exposure to e-cigarettes that resulted in lung disease. The articles were selected by independent assessment, reading first the titles and abstracts, then the full text of the selected articles. The Newcastle-Ottawa Scale was used to assess study quality, and the included studies received between 5 and 7 stars. Finally, the data were extracted for meta-analysis. Initially, 61 articles were found and 6 were considered eligible, all of which were cross-sectional and applied questionnaires. The meta-analysis found a significant association between electronic cigarette use and asthma exacerbation (adjusted OR 1.44 95% CI 1.17 - 1.76). However, no studies evaluated the association with other lung diseases, including electronic cigarette or vaping product use-associated lung injury in adolescents. The metaanalysis revealed a significant association between e-cigarette use and asthma exacerbation among adolescents with chronic asthma, as well as among their previously healthy peers.

Principales tecnologías de indización en las ciencias de la salud que se emplean en Occidente / Main indexing healthcare technologies used in West

En la actualidad, las tecnologías de indización en las ciencias de la salud están aportando mu-chos beneficios para el ámbito biomédico y la estandarización de su correspondiente termino-logía, puesto que esta cuestión es fundamental para lograr un diagnóstico médico más preciso e inequívoco Por esta razón, en este artículo se ha explicado con detalle cómo funcionan estas tecnologías: Terminología Anatómica In-ternacional (TAI), Medical Subject Headings y el Systematized Nomenclature of Medicine Cli-nical terminology (SNOMED CT), así como, las razones de la importancia de su uso para los sanitarios y los terminólogos.(AU)

Nowadays, healthcare indexing technologies are profiting the biomedical field and the stand-ardization of its corresponding terminology, since this is essential to achieve a more pre-cise and unequivocal medical diagnosis. Thus, in this article it has been performed a thorough explanation on how these healthcare technolo-gies work: International Anatomical Terminology (TAI), Medical Subject Headings and the Sys-tematised Nomenclature of Medicine Clinical terminology (SNOMED CT), as well as it was elucidated the reasons of its use for healthcare professionals and terminologists.(AU)

A comparison of 3 productive authors' research domains based on sources from articles, cited references and citing articles using social network analysis.

BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Citation analysis of the 100 top-cited articles on the topic of hidradenitis suppurativa since 2013 using Sankey diagrams: Bibliometric analysis.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.