Análisis bibliométrico de la investigación española de impacto en Enfermería Educativa / Bibliometric analysis of the Spanish research with impact in Nursing Educatio

El objetivo de este estudio es elaborar una revisión bibliométrica de la producción española en Enfermería Educativa. Se indaga el tópico de la Enfermería Educativa española a través de documentos indexados en las bases de la Web of Science (WoS) desde 1991 a 2021 usando un diseño descriptivo y cuantitativo (bibliométrico) en una muestra de 927 documentos (artículos, ensayos clínicos y revisiones). La producción diacrónica muestra una tendencia creciente. Las universidades más productivas suelen ser las del arco mediterráneo español. Nurse Education Today es la revista más editora y también la más citada. El promedio de citas por documento es 11 y un índice h de Hirsch de 46. Se exponen los documentos más citados. Se confirma la Enfermería Educativa española como un tópico de investigación científica relevante (hot topic) homologable a cualquier tópico cualificado de las disciplinas de las Ciencias de la Salud.(AU)

The objective of this study is to elaborate a bibliometric review of the Spanish production in Educational Nursing. The topic of Spanish Nursing Education is explored through research documents indexed in the bases of the Web of Science from 1991 to 2021 using a descriptive and quantitative (bibliometric) design with a sample of 927 documents (articles, clinical trials and reviews). Longitudinal production shows an increasing trend. The most productive universities are from the Spanish Mediterranean arc. The journal Nurse Education Today is the most important publisher and the most cited journal. The average number of citations per document is 11 citations with a Hirsch h-index of 46. Spanish Nursing Education is confirmed as a relevant scientific research topic (hot topic) with bibliometric patterns comparable to those of any qualified topic of the Health Sciences.(AU)

Biomolecular Prospecting, Informative Gaps, and the Cancer Clinic: A Qualitative Fieldwork and an Epistemological, Historical and Ethical Analysis of Informed Consent for Clinical Trials for Monoclonal Antibodies and Biobank Research.

What happens to patients with cancer engaged in biomedical research when intellectual property regimes and ethical regimes intersect? This qualitative historical study addresses this question by situating the experiences, hopes, and reasons of patients to enter clinical trials within the historical trajectory of informed consent and monoclonal antibodies, the biotechnology underpinning many targeted drugs used in oncological clinical trials and biobank research. Based on fieldwork we undertook in a German university hospital where we interviewed patients and the medical personnel, a historical review, and an ethical analysis we inquire into the effects that financial, legal, and technological changes connected to the relevant pharmaceutical research and commerce have on cancer patients engaged in clinical trials and biobank research. We find that the controversial aspects of monoclonal antibodies, especially those related to the commercial interests at stake, enter the informed consent process mainly in the form of informative gaps. We highlight how a qualitative analysis of the clinic, especially when it is situated against the backdrop of the history of related technological advancements and patent regime, it can serve the purpose of giving voice to subjects who are silenced by regimes of an ethical, epistemic, and commercial kind while pointing to informed consent as an unhelpful device for addressing risks arising from the commercial purposes of biomedical products and infrastructure.

Development of a novel database to review and assess the clinical effects of EPA and DHA omega-3 fatty acids.

Due to their multiple mechanisms of biological action, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been the focus of ongoing active research for decades. In spite of the resulting body of knowledge, there remain significant gaps in our understanding of EPA/DHA health effects. Further, the volume of existing research makes it challenging to conduct systematic investigations to identify or resolve those gaps. The purpose of this article is to introduce the GOED Clinical Study Database (CSD), a comprehensive, manually-curated relational database that catalogs this research.

Marketing methods for electronic resources in medical libraries: a study on the application of the analytical hierarchy process.

Objective: Paired with the high cost of providing access to electronic resources in medical libraries, the inefficient use of these resources highlights the need for more efforts to promote these resources than ever before. In this study, electronic resource marketing methods were prioritized and the best strategies were determined using the analytical hierarchy process (AHP). Methods: Using an analytical survey of officials of medical libraries, the most common methods for marketing electronic resources in libraries were determined and divided into categories of strategies. Five important criteria for marketing strategies were also selected. Using the analytical hierarchy process, pairwise comparisons were performed between the alternatives (i.e., strategies), which were evaluated against the selected criteria. Data analysis was performed using Expert Choice 11 software. Results: A total of 44 electronic resource marketing methods were identified and categorized into 4 strategies. On average, 43.9% of these methods were used by the surveyed libraries. The analytical hierarchy process showed that simplicity was the most important criterion and that communication networks were the best electronic resource marketing strategy. Home/off-campus access, group training, library search stations, and marketing by individual librarians were the most preferred methods of marketing electronic resources. Conclusion: With the availability of a variety of different methods for marketing electronic resources, medical libraries must select strategies based on important criteria depending on the characteristics of the library, librarians, and users. Thus, the analytical hierarchy process can be an effective and practical solution to decision-making by mathematically prioritizing the selection of the best strategies from a set of alternatives based on differentially weighted criteria.

Peer review of searches for studies for health technology assessments, systematic reviews, and other evidence syntheses.

INTRODUCTION: Peer review of searches is a process whereby both the search strategies and the search process description are reviewed, ideally using an evidence-based checklist. RATIONALE: As the search strategy underpins any well-conducted evidence synthesis, its quality could affect the final result. Evidence shows, however, that search strategies are prone to error. FINDINGS: There is increasing awareness and use of the PRESS Evidence-Based Checklist and peer review of search strategies, at the outset of evidence syntheses, prior to the searches being run, and this is now recommended by a number of evidence synthesis organizations. RECOMMENDATIONS AND CONCLUSIONS: Searches for evidence syntheses should be peer reviewed by a suitably qualified and experienced librarian or information specialist after being designed, ideally, by another suitably qualified and experienced librarian or information specialist. Peer review of searches should take place at two important stages in the evidence synthesis process; at the outset of the project prior to the searches being run and at the prepublication stage. There is little empirical evidence, however, to support the effectiveness of peer review of searches. Further research is required to assess this. Those wishing to stay up to date with the latest developments in information retrieval, including peer review of searches, should consult the SuRe Info resource (http://www.sure-info.org), which seeks to help information specialists and others by providing easy access to the findings from current information retrieval methods research and thus support more research-based information retrieval practice.

Trend of transplants and organ and tissue donations in Brazil: a time series analysis / Tendencia de trasplantes y donaciones de órganos y tejidos en Brasil: análisis de la serie de tiempos / Tendência dos transplantes e doações de órgãos e tecidos no Brasil: análise de séries temporais

ABSTRACT Objectives: to indentify the time trend of rates of organs and tissues effective donors, of reports and types of transplanted organs per million people of the Brazilian population. Methods: ecological study, of time series, about reports of organ donations and on transplants. The data were provided by the Registro Brasileiro de Transplantes and analyzed using polynomial regression. Results an increasing trend was found for potential donors and effective donors, with an average increase of 2.33 and 0.92 per year, respectively. The South Region had the highest rate of potential donors (83.8) and effective donors (34.1) and the North Region, the lowest rate (20.2 and 3.9). The family refusal was the main obstacle to accomplish the donation. Conclusions the results show an increasing trend of potential donors and effective donors throughout Brazil, with emphasis on the southern region of the country. Among the main reasons for non-donation, it is worth emphasizing family refusal and medical contraindication prescription.

RESUMEN Objetivos: identificar la tendencia temporal de las tasas de donantes efectivos de órganos y tejidos, de notificaciones y tipos de órganos trasplantados por millón de población en Brasil. Métodos: estudio ecológico, de series temporales, sobre notificaciones de donaciones y trasplantes de órganos. Los datos fueron proporcionados por el Registro Brasileño de Trasplantes, analizados mediante regresión polinomial. Resultados: se detectó una tendencia creciente para donantes potenciales y donantes efectivos, con un incremento promedio de 2,33 y 0,92 por año. La Región Sur tuvo la tasa más alta de donantes potenciales (83,8) y donantes efectivos (34,1) y la Región Norte, la más baja (20,2 y 3,9). La negativa familiar fue el principal impedimento para efectuar la donación. Conclusiones: los resultados demuestran una tendencia creciente de donantes potenciales y donantes efectivos en todo Brasil, con énfasis en la Región Sur. Entre los principales motivos para no donar destacan el rechazo familiar y contraindicación médica.

RESUMO Objetivos: identificar a tendência temporal das taxas de doadores efetivos de órgãos e tecidos, de notificações e tipos de órgãos transplantados por milhão da população no Brasil. Métodos: estudo ecológico, de séries temporais, sobre notificações de doações de órgãos e transplantes. Os dados foram fornecidos pelo Registro Brasileiro de Transplantes e analisados por meio de regressão polinomial. Resultados detectou-se tendência crescente de potenciais doadores e doadores efetivos, com aumento médio ao ano de 2,33 e 0,92, respectivamente. A Região Sul apresentou a maior taxa de potenciais doadores (83,8) e doadores efetivos (34,1) e a Região Norte, a menor (20,2 e 3,9). A recusa familiar consistiu no principal impedimento para efetivar a doação. Conclusões os resultados demonstram tendência crescente de potenciais doadores e doadores efetivos em todo o Brasil, com destaque para a região Sul. Dentre os principais motivos para a não doação, destacam-se a recusa familiar e a contraindicação médica.

JIB.tools 2.0 - A Bioinformatics Registry for Journal Published Tools with Interoperability to bio.tools.

JIB.tools 2.0 is a new approach to more closely embed the curation process in the publication process. This website hosts the tools, software applications, databases and workflow systems published in the Journal of Integrative Bioinformatics (JIB). As soon as a new tool-related publication is published in JIB, the tool is posted to JIB.tools and can afterwards be easily transferred to bio.tools, a large information repository of software tools, databases and services for bioinformatics and the life sciences. In this way, an easily-accessible list of tools is provided which were published in JIB a well as status information regarding the underlying service. With newer registries like bio.tools providing these information on a bigger scale, JIB.tools 2.0 closes the gap between journal publications and registry publication. (Reference: https://jib.tools).

Trial2rev: Combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews.

OBJECTIVES: Systematic reviews of clinical trials could be updated faster by automatically monitoring relevant trials as they are registered, completed, and reported. Our aim was to provide a public interface to a database of curated links between systematic reviews and trial registrations. MATERIALS AND METHODS: We developed the server-side system components in Python, connected them to a PostgreSQL database, and implemented the web-based user interface using Javascript, HTML, and CSS. All code is available on GitHub under an open source MIT license and registered users can access and download all available data. RESULTS: The trial2rev system is a web-based interface to a database that collates and augments information from multiple sources including bibliographic databases, the ClinicalTrials.gov registry, and the actions of registered users. Users interact with the system by browsing, searching, or adding systematic reviews, verifying links to trials included in the review, and adding or voting on trials that they would expect to include in an update of the systematic review. The system can trigger the actions of software agents that add or vote on included and relevant trials, in response to user interactions or by scheduling updates from external resources. DISCUSSION AND CONCLUSION: We designed a publicly-accessible resource to help systematic reviewers make decisions about systematic review updates. Where previous approaches have sought to reactively filter published reports of trials for inclusion in systematic reviews, our approach is to proactively monitor for relevant trials as they are registered and completed.

Overcoming the data drought: exploring general practice in Australia by network analysis of big data.

OBJECTIVES: To investigate the organisation and characteristics of general practice in Australia by applying novel network analysis methods to national Medicare claims data. DESIGN: We analysed Medicare claims for general practitioner consultations during 1994-2014 for a random 10% sample of Australian residents, and applied hierarchical block modelling to identify provider practice communities (PPCs). PARTICIPANTS: About 1.7 million patients per year. MAIN OUTCOME MEASURES: Numbers and characteristics of PPCs (including numbers of providers, patients and claims), proportion of bulk-billed claims, continuity of care, patient loyalty, patient sharing. RESULTS: The number of PPCs fluctuated during the 21-year period; there were 7747 PPCs in 2014. The proportion of larger PPCs (six or more providers) increased from 32% in 1994 to 43% in 2014, while that of sole provider PPCs declined from 50% to 39%. The median annual number of claims per PPC increased from 5000 (IQR, 40-19 940) in 1994 to 9980 (190-23 800) in 2014; the proportion of PPCs that bulk-billed all patients was lowest in 2004 (21%) and highest in 2014 (29%). Continuity of care and patient loyalty were stable; in 2014, 50% of patients saw the same provider and 78% saw a provider in the same PPC for at least 75% of consultations. Density of patient sharing in a PPC was correlated with patient loyalty to that PPC. CONCLUSIONS: During 1994-2014, Australian GP practice communities have generally increased in size, but continuity of care and patient loyalty have remained stable. Our novel approach to the analysis of routinely collected data allows continuous monitoring of the characteristics of Australian general practices and their influence on patient care.

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.

BACKGROUND: Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. OBJECTIVE: The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. METHODS: PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET's phenotype representation with PheKnow-Cloud's by using PheKnow-Cloud's experimental setup. In PIVET's framework, we also introduce a statistical model trained on domain expert-verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. RESULTS: PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET's analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes. CONCLUSIONS: Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.

Establishment and application of a database for hepatitis C virus NS3/4A protease inhibitors and their drug resistance data / 临床肝胆病杂志

ObjectiveTo establish a database for direct-acting antiviral agents (DAAs) targeting hepatitis C virus (HCV) NS3/4A protease and related resistance-associated variants, and to investigate its application in drug resistance analysis. MethodsThe published data and information of anti-NS3/4A DAAs and related drug resistance data were collected and mined. The in vitro data of viral drug-resistant mutations and resistance-associated variants identified in clinical treatment were entered into the database, and a statistical analysis was performed based on the type of drugs, HCV genotypes, positions of drug-resistant mutations, and type of substituted amino acids. Some of the results were available for online query on a website. Then the database was used to perform a multi-data analysis of the drug resistance of genotype 3 HCV, a well-known difficult-to-treat viral genotype. ResultsA database for anti-NS3/4A DAAs and their drug resistance data was established and some data were available for online query on a website (http://www.biosino.org/hcv/). This database consisted of the following four parts, with over ten thousands of pieces of information: the information of DAAs; the in vitro drug-resistance data of viral strains with different genotypes containing drug-resistant mutations; the prevalence of pre-existing resistance-associated variants and their detection rates in patients with treatment failure; the three-dimensional structures of the DAA-NS3/4A protease complex. This database was used to analyze drug resistance of all genotypes of HCV, and it was found that anti-NS3/4A DAAs had the poorest therapeutic effect in patients with genotype 3 HCV. Although the third-generation anti-NS3/4A DAAs had a good antiviral effect in patients with wild-type genotype 3 HCV, drug-resistant mutations might occur. ConclusionThis database is the first one in China for anti-NS3/4A DAAs and their drug-resistance data and provides an important resource of information and guidance for research on drug resistance and clinical treatment of HCV.

The Patient Repository for EEG Data + Computational Tools (PRED+CT).

Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canonical neural computations, making them a uniquely insightful measure of brain function. As evidence of these virtues, numerous candidate biomarkers of different psychiatric and neurological diseases have been advanced. Presumably, we would only need to apply powerful machine-learning methods to validate these ideas and provide novel clinical tools. Yet, the reality of this advancement is more complex: the scale of data required for robust and reliable identification of a clinical biomarker transcends the ability of any single laboratory. To surmount this logistical hurdle, collective action and transparent methods are required. Here we introduce the Patient Repository of EEG Data + Computational Tools (PRED+CT: predictsite.com). The ultimate goal of this project is to host a multitude of available tasks, patient datasets, and analytic tools, facilitating large-scale data mining. We hope that successful completion of this aim will lead to the development of novel EEG biomarkers for differentiating populations of neurological and psychiatric disorders.

Information Resources for the Exotic Animal Practitioner.

An essential component of evidence-based practice is finding the best available evidence to answer a clinical question. Finding evidence is difficult for veterinarians in general, and exotic animal clinicians in particular, owing to the lack of studies that provide a high level of clinically relevant evidence and limited access to resources. Knowing where and how to search for evidence can facilitate evidence-based practice.

[Personalization in the medicine of the future : Opportunities and risks]. / Personalisierung in der Medizin der Zukunft : Chancen und Risiken.

Personalized medicine is not a new concept. The renaissance of the term is due to the enormous progress in gene sequencing technology and functional imaging, as well as the development of targeted therapies. Application of these technologies in clinical medicine will necessitate infrastructural as well as organizational and educational changes in the healthcare system. An important change required already in the short-term is the introduction of centralized structures, preferably in university clinics, which adopt these innovations and incorporate them into clinical care. Simultaneously, the collation and use of large quantities of relevant data from highly variable sources must be successfully mastered, in order to pave the way for disruptive technologies such as artificial intelligence.

Educación continua: bases de datos biomédicas II: Medline/PubMed / Continuing education: biomedical databases II: Medline/PubMed

MEDLINE/PubMed es una de las bases de datos de bibliografía biomédica más grandes e importantes y el principal componente de PUbMed que cubre las áreas de medicina, enfermería, odontología, veterinaria, sistemas de salud y ciencias preclínicas. La interfaz de PubMed permite realizar búsquedas, no solamente en Medline, sino también en otras 35 bases de datos del NCBI (Centro Nacional para la Información Biotecnológica). Medline fue desarrollado por la Biblioteca Nacional de Medicina de los Estados Unidos (NLM), que forma parte del Instituto Nacional de Salud (NIH). En esta sexta entrega de educación continua se describen la estructura, el funcionamiento y las características de los principales componentes de Medline/PubMed que permitirán al usuario realizar una búsqueda bibliográfica más eficiente.

MEDLINE / PubMed is one of the largest and most important biomedical bibliographic databases and the main component of PUbMed that covers the areas of medicine, nursing, dentistry, veterinary, health systems and preclinical sciences. The PubMed interface allows searching not only of Medline, but also of another 35 NCBI (National Center for Biotechnology Information) databases. Medline was developed by the National Library of Medicine of the United States (NLM), which is part of the National Institute of Health (NIH). This sixth edition of continuing education describes the structure, operation and characteristics of the main components of Medline / PubMed that will allow the user to perform a more efficient bibliographic search.

Increasing number of databases searched in systematic reviews and meta-analyses between 1994 and 2014.

OBJECTIVES: The purpose of this study was to determine whether the number of bibliographic databases used to search the health sciences literature in individual systematic reviews (SRs) and meta-analyses (MAs) changed over a twenty-year period related to the official 1995 launch of the Cochrane Database of Systematic Reviews (CDSR). METHODS: Ovid MEDLINE was searched using a modified version of a strategy developed by the Scottish Intercollegiate Guidelines Network to identify SRs and MAs. Records from 3 milestone years were searched: the year immediately preceding (1994) and 1 (2004) and 2 (2014) decades following the CDSR launch. Records were sorted with randomization software. Abstracts or full texts of the records were examined to identify database usage until 100 relevant records were identified from each of the 3 years. RESULTS: The mean and median number of bibliographic databases searched in 1994, 2004, and 2014 were 1.62 and 1, 3.34 and 3, and 3.73 and 4, respectively. Studies that searched only 1 database decreased over the 3 milestone years (60% in 1994, 28% in 2004, and 10% in 2014). CONCLUSIONS: The number of bibliographic databases searched in individual SRs and MAs increased from 1994 to 2014.