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.

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.

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.

UNASSIGNED: After conducting a comprehensive literature search of two medical electronic databases, PubMed and Embase, as well as two citation databases, Web of Science Core Collections (WoS) and Scopus, we aimed to conduct an Altmetric and Scientometric analysis of the History of Medicine literature in medical research.
UNASSIGNED: The following software tools were used for analyzing the retrieved records from PubMed and Embase databases and conducting a collaboration analysis to identify the countries involved in scientific medical papers, as well as clustering keywords to reveal the trend of History of Medicine research for the future. These software tools (VOSviewer 1.6.18 and Spss 16) allowed the researchers to visualize bibliometric networks, perform statistical analysis, and identify patterns and trends in the data.
UNASSIGNED: Our analysis revealed 53,771 records from PubMed and 54,405 records from EMBASE databases retrieved in the field of History of Medicine by 105,286 contributed authors in WoS. We identified 157 countries that collaborated on scientific medical papers. By clustering 59,995 keywords, we were able to reveal the trend of History of Medicine research for the future. Our findings showed a positive association between traditional bibliometrics and social media metrics such as the Altmetric Attention Score in the History of Medicine literature (p < 0.05).
UNASSIGNED: Sharing research findings of articles in social scientific networks will increase the visibility of scientific works in History of Medicine research, which is one of the most important factors influencing the citation of articles. Additionally, our overview of the literature in the medical field allowed us to identify and examine gaps in the History of Medicine research.

Using two databases, this bibliometric analysis was done for the papers published by the Faculty of Medicine, University of Khartoum (FMUK), from 2019 to 2023. Data were extracted from SCImago for all Sudan, and from PubMed for the publications by FMUK and its associated research centres, the Institute of Endemic Diseases, and the Mycetoma Research Center. The analysis of publications included the count and type of publications, the journals, and national and international collaboration assessment. The publications from FMUK show improvement over time in number and quality, a growth that is significantly influenced by national and international collaboration. These partnerships have proven to be a key driver of FMUK\'s research output, together with the valuable contributions of the specialized research institutions. However, there is room for improvement in the research output by increasing institutional capacity to support research and scientific communication. The Sudanese Journal of Paediatrics is an example where open access has a positive impact by allowing peripheral journals to be established despite the constraints.

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X\'s predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user\'s web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.