Survey research enables the gathering of information on individual perspectives in a large cohort. It can be epidemiological, attitude or knowledge focussed. Assessment of survey studies sampling neurosurgeons is currently lacking in the literature. This study aimed to highlight the characteristics, quality, and citation predictors of the most influential survey research studies published in the neurosurgical literature. Using PubMed and Google Scholar, the 50 most cited survey research publications were identified and reviewed. Data relating to the characteristics of the articles, participants and questionnaires were retrieved. The studies\' quality and citation patterns were assessed. The median articles\' age and publishing journal impact factor (IF) were 15.5 years and 2.82, respectively. Thirty-two (64%) articles were first authored by researchers from the USA while 28(56%) studies were focussed on specific disease management. The median number of participants and response rates were 222 and 51%, respectively. A full version of the questionnaire was provided in 18 (36%) articles. Only four (8%) articles reported validation of the questionnaire. The overall quality of reporting of the surveys was considered fair (based on good grading in five parameters, fair grading in one parameter, and poor grading in four parameters). The median citation number was 111. The citation analysis showed that the participant number, article age (≥15.5 years), and questionnaire category (surgical complications) were significant predictors of citation numbers. The citation rates were not influenced by the response rates or the journal\'s IF. In conclusion, high-impact survey publications in the neurosurgical literature were moderately cited and of fair quality. Their citation numbers were not affected by response rates but were positively influenced by the publication age, number of participants, and by novel data or the questions raised in the survey category. Surveys are valuable forms of research that require extensive planning, time, and effort in order to produce meaningful results. Increasing awareness of the factors that could affect citations may be useful to those who wish to undertake survey research.

The reason for existence of a scientific journal is to disseminate knowledge of science. The objective is a continuous improvement in the quality of what is published and an increase in the number of readers. In this sense, the impact factor is an indicator that helps us visualize the improvement in the quality of our journal. To improve this impact factor, indexing in the most used databases is very important.Farmacéuticos Comunitarios is indexed in several index journals but we were missing the most used ones. In this last year we have requested indexing in the most important ones, SCOPUS and Medline. In November we received SCOPUS approval and are close to getting it on Medline. In September we were approved by the editorial criteria and in December by the scientists. We only need the technical criteria to be able to appear in Medline and in its online version PubMed. We are working on it and it is very likely that within a year we will be indexed in PubMed.

The automatic recognition of biomedical relationships is an important step in the semantic understanding of the information contained in the unstructured text of the published literature. The BioRED track at BioCreative VIII aimed to foster the development of such methods by providing the participants the BioRED-BC8 corpus, a collection of 1000 PubMed documents manually curated for diseases, gene/proteins, chemicals, cell lines, gene variants, and species, as well as pairwise relationships between them which are disease-gene, chemical-gene, disease-variant, gene-gene, chemical-disease, chemical-chemical, chemical-variant, and variant-variant. Furthermore, relationships are categorized into the following semantic categories: positive correlation, negative correlation, binding, conversion, drug interaction, comparison, cotreatment, and association. Unlike most of the previous publicly available corpora, all relationships are expressed at the document level as opposed to the sentence level, and as such, the entities are normalized to the corresponding concept identifiers of the standardized vocabularies, namely, diseases and chemicals are normalized to MeSH, genes (and proteins) to National Center for Biotechnology Information (NCBI) Gene, species to NCBI Taxonomy, cell lines to Cellosaurus, and gene/protein variants to Single Nucleotide Polymorphism Database. Finally, each annotated relationship is categorized as \'novel\' depending on whether it is a novel finding or experimental verification in the publication it is expressed in. This distinction helps differentiate novel findings from other relationships in the same text that provides known facts and/or background knowledge. The BioRED-BC8 corpus uses the previous BioRED corpus of 600 PubMed articles as the training dataset and includes a set of newly published 400 articles to serve as the test data for the challenge. All test articles were manually annotated for the BioCreative VIII challenge by expert biocurators at the National Library of Medicine, using the original annotation guidelines, where each article is doubly annotated in a three-round annotation process until full agreement is reached between all curators. This manuscript details the characteristics of the BioRED-BC8 corpus as a critical resource for biomedical named entity recognition and relation extraction. Using this new resource, we have demonstrated advancements in biomedical text-mining algorithm development. Database URL: https://codalab.lisn.upsaclay.fr/competitions/16381.

In the neurosciences, concepts play an important role in the conception and direction of research. Among the theoretical notions and direction of research, plasticity stands out because of the multiple ways in which scientists use it to describe and interpret how the nervous system changes and adapts to different requirements. The occurrence of different conceptualizations of plasticity in the scientific literature during the second half of the twentieth century and up to the present was investigated using bibliometric methods. Throughout the period analyzed, synaptic plasticity has remained the dominant conceptualization of plasticity. However, scientists have continued to introduce novel plasticity concepts reflecting the scientific advances they have made in understanding the dynamic nature of the nervous system. The conceptual evolution of plasticity documents that the view of the adult nervous system as immutable has been replaced by an understanding of the nervous system as capable of lifelong change and adaptation.

UNASSIGNED: Libraries provide access to databases with auto-cite features embedded into the services; however, the accuracy of these auto-cite buttons is not very high in humanities and social sciences databases.
UNASSIGNED: This case compares two biomedical databases, Ovid MEDLINE and PubMed, to see if either is reliable enough to confidently recommend to students for use when writing papers. A total of 60 citations were assessed, 30 citations from each citation generator, based on the top 30 articles in PubMed from 2010 to 2020.
UNASSIGNED: Error rates were higher in Ovid MEDLINE than PubMed but neither database platform provided error-free references. The auto-cite tools were not reliable. Zero of the 60 citations examined were 100% correct. Librarians should continue to advise students not to rely solely upon citation generators in these biomedical databases.

OBJECTIVE: To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH).
METHODS: High priority SDoH terms and definitions were collated from authoritative sources, curated based on publication frequencies, and refined by subject matter experts. Descriptive analyses were used to investigate how PubMed search details and best match results were affected by the addition of SDoH concepts to MeSH. Three information retrieval metrics (Precision, Recall, and F measure) were used to quantitatively assess the accuracy of PubMed search results. Pre- and post-update documents were clustered into topic areas using a Natural Language Processing pipeline, and SDoH relevancy assessed.
RESULTS: Addition of 35 SDoH terms to MeSH resulted in more accurate algorithmic translations of search terms and more reliable best match results. The Precision, Recall, and F measures of post-update results were significantly higher than those of pre-update results. The percentage of retrieved publications belonging to SDoH clusters was significantly greater in the post- than pre-update searches.
CONCLUSIONS: This evaluation confirms that inclusion of new SDoH terms in MeSH can lead to qualitative and quantitative enhancements in PubMed search retrievals. It demonstrates the methodology for and impact of suggesting new terms for MeSH indexing. It provides a foundation for future efforts across behavioral and social science research (BSSR) domains.
CONCLUSIONS: Improving the representation of BSSR terminology in MeSH can improve PubMed search results, thereby enhancing the ability of investigators and clinicians to build and utilize a cumulative BSSR knowledge base.

Mental health conditions, such as depression, anxiety, and stress-related disorders, are often difficult to diagnose and monitor using traditional methods. Salivary biomarkers offer a promising alternative due to their non-invasive nature, ease of collection, and the potential to reflect real-time physiological changes associated with mental health. This bibliometric analysis examines 95 clinical trials on stress biomarkers for mental health, published between 2003 and 2024. The field is characterized by extensive collaboration and global participation, involving 593 authors and publications across 73 journals. Despite a consistent annual publication rate, notable increases in 2011, 2014, and 2018 indicate growing research interest. The United States leads in research output, followed by Australia, Germany, and Japan, with Psychoneuroendocrinology being the most prominent journal. Co-occurrence analysis identified nine research clusters, suggesting diverse directions such as the impact of stress-related hormones, circadian rhythms, mindfulness, various therapies, aging, psychological adaptation mechanisms, exercise therapy, anxiety disorders, and the autonomic nervous system on salivary biomarkers. Key terms such as \"biomarkers/metabolism,\" AND \"hydrocortisone/metabolism,\" AND \"saliva/metabolism\" were central, with significant activity from 2012 to 2018. This analysis highlights a growing focus on the metabolic processes and therapeutic applications of salivary biomarkers in mental health. This bibliometric analysis calls attention to the promising potential of salivary biomarkers to revolutionize mental health diagnostics and treatment through non-invasive methods, fostering interdisciplinary research, technological advancements, and global health improvements.

The number of publications in biomedicine and life sciences has grown so much that it is difficult to keep track of new scientific works and to have an overview of the evolution of the field as a whole. Here, we present a two-dimensional (2D) map of the entire corpus of biomedical literature, based on the abstract texts of 21 million English articles from the PubMed database. To embed the abstracts into 2D, we used the large language model PubMedBERT, combined with t-SNE tailored to handle samples of this size. We used our map to study the emergence of the COVID-19 literature, the evolution of the neuroscience discipline, the uptake of machine learning, the distribution of gender imbalance in academic authorship, and the distribution of retracted paper mill articles. Furthermore, we present an interactive website that allows easy exploration and will enable further insights and facilitate future research.

BACKGROUND: The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice.
METHODS: In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies.
RESULTS: From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs.
CONCLUSIONS: Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.

OBJECTIVE: Periodically, medical publications are retracted. The reasons vary from minor situations, such as author attributions, which do not undermine the validity of the data or the analysis in the article, to serious reasons, such as fraud. Understanding the reasons for retraction can provide important information for clinicians, educators, researchers, journals, and editorial boards.
METHODS: The PubMed database was searched using the term \"COVID-19\" (coronavirus disease 2019) and the term limitation \"retracted publication.\" The characteristics of the journals with retracted articles, the types of article, and the reasons for retraction were analyzed.
RESULTS: This search recovered 196 articles that had been retracted. These retractions were published in 179 different journals; 14 journals had >1 retracted article. The mean impact factor of these journals was 8.4, with a range of 0.32-168.9. The most frequent reasons for retractions were duplicate publication, concerns about data validity and analysis, concerns about peer review, author request, and the lack of permission or ethical violation. There were significant differences between the types of article and the reasons for retraction but no consistent pattern. A more detailed analysis of two particular retractions demonstrates the complexity and the effort required to make decisions about article retractions.
CONCLUSIONS: The retraction of published articles presents a significant challenge to journals, editorial boards, peer reviewers, and authors. This process has the potential to provide important benefits; it also has the potential to undermine confidence in both research and the editorial process.