Retrieval of overviews of systematic reviews in MEDLINE was improved by the development of an objectively derived and validated search strategy

ObjectivesLocating overviews of systematic reviews is difficult because of an absence of appropriate indexing terms and inconsistent terminology used to describe overviews. Our objective was to develop a validated search strategy to retrieve overviews in MEDLINE.Study Design and SettingWe derived a test set of overviews from the references of two method articles on overviews. Two population sets were used to identify discriminating terms, that is, terms that appear frequently in the test set but infrequently in two population sets of references found in MEDLINE. We used text mining to conduct a frequency analysis of terms appearing in the titles and abstracts. Candidate terms were combined and tested in MEDLINE in various permutations, and the performance of strategies measured using sensitivity and precision.ResultsTwo search strategies were developed: a sensitivity-maximizing strategy, achieving 93% sensitivity (95% confidence interval [CI]: 87, 96) and 7% precision (95% CI: 6, 8), and a sensitivity-and-precision–maximizing strategy, achieving 66% sensitivity (95% CI: 58, 74) and 21% precision (95% CI: 17, 25).ConclusionThe developed search strategies enable users to more efficiently identify overviews of reviews compared to current strategies. Consistent language in describing overviews would aid in their identification, as would a specific MEDLINE Publication Type.     

Is autoimmunology a discipline of its own? A big data-based bibliometric and scientometric analyses

Autoimmunology is a super-specialty of immunology specifically dealing with autoimmune disorders. To assess the extant literature concerning autoimmune disorders, bibliometric and scientometric analyses (namely, research topics/keywords co-occurrence, journal co-citation, citations, and scientific output trends – both crude and normalized, authors network, leading authors, countries, and organizations analysis) were carried out using open-source software, namely, VOSviewer and SciCurve. A corpus of 169,519 articles containing the keyword “autoimmunity” was utilized, selecting PubMed/MEDLINE as bibliographic thesaurus. Journals specifically devoted to autoimmune disorders were six and covered approximately 4.15% of the entire scientific production. Compared with all the corpus (from 1946 on), these specialized journals have been established relatively few decades ago. Top countries were the United States, Japan, Germany, United Kingdom, Italy, China, France, Canada, Australia, and Israel. Trending topics are represented by the role of microRNAs (miRNAs) in the ethiopathogenesis of autoimmune disorders, contributions of genetics and of epigenetic modifications, role of vitamins, management during pregnancy and the impact of gender. New subsets of immune cells have been extensively investigated, with a focus on interleukin production and release and on Th17 cells. Autoimmunology is emerging as a new discipline within immunology, with its own bibliometric properties, an identified scientific community and specifically devoted journals.     

Generating disease-pertinent treatment vocabularies from MEDLINE citations

ObjectiveHealthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies.MethodsA pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches.ResultsThe pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p = 0.53). However, the pipeline-based system had significantly better performance than the two baselines.ConclusionsThe pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.     

2001—2010年MEDLINE收录某医院科技论文文献计量学分析

目的通过分析2001—2010年某医院科技论文被国际权威检索工具MEDLINE收录情况,以评价该院科研水平和学术地位,为科研管理部门提供信息参考。方法应用文献计量学和数理统计方法对2001—2010年MEDLINE收录某军区医院科技论文进行统计分析。结果近10年MEDLINE一共收录该院科技论文225篇,其中中文文献148篇、英文文献77篇,国内刊物发表文献166篇、国外刊物发表文献59篇,主要以国内期刊和中文文献为主;近5年来英文文献和国外刊物发表文献有所上升,其中国外刊物以美国、英格兰、荷兰等欧美国家为主;论文合作度达5.3,论文合作率达99.1%。结论近10年来,该院科研水平发展整体处在上升趋势,还应继续提高在国外刊物上的载文量,促进医院科研水平和学术地位的持续协调发展。     

探讨Meta分析的循证检索

目的:以有效检索策略为基础的Meta分析,所做的临床决策会对患者产生积极的疗效。文章调查了Meta分析报道检索策略效果证据的范畴。资料来源:对1995-01-01/2004-12-3110年间,EBSCO数据库MED-LINE全文子集进行检索。资料选择:对MEDLINE全文子集中,标引“Meta分析”为出版物类型的论文,进行随机抽样调查(n=100)。资料提炼:以3种方法对这些论文进行分类:论文A类:既足够详细地报道了检索策略(以便于它可能被重复利用),又提供了检索策略效果的证据;论文B类:足够详细地报道了检索策略,但没有提供检索策略效果的证据;论文C类:既没有足够详细地报道检索策略,也没有提供检索策略效果的证据。被分为A类的论文根据所报道的证据水平进一步再分类。资料综合:在最后分析的93篇论文样本中,8篇(8.6%)被分为A类,57(61.3%)篇被分为B类,28篇(30.1%)被分为C类。A类中的论文报道了先前有效的、公开发表了的或者基于专家意见的检索策略。结论:专家评审标准一定要力争得到进一步发展,那就要求Meta分析的作者,对他们检索策略的效果证据要加以报道。     

Sequential result refinement for searching the biomedical literature

Information overload is a problem for users of MEDLINE, the database of biomedical literature that indexes over 17 million articles. Various techniques have been developed to retrieve high quality or important articles. Some techniques rely on using the number of citations as a measurement of an article’s importance. Unfortunately, citation information is proprietary, expensive, and suffers from “citation lag.” MEDLINE users have a variety of information needs. Although some users require high recall, many users are looking for a “few good articles” on a topic. For these users, precision is more important than recall. We present and evaluate a method for identifying articles likely to be highly cited by using information available at the time of listing in MEDLINE. The method uses a score based on Medical Subject Headings (MeSH) terms, journal impact factor (JIF), and number of authors. This method can filter large MEDLINE result sets (>1000 articles) returned by actual user queries to produce small, highly cited result sets.     

DIALOG系统医药品安全信息探析

介绍DIALOG系统医药品安全方面信息的获取,包括MEDLINE、EMBASE等医药品文献数据库及其它类型数据库如时事通讯、副作用反应数据库,以利于医药品安全信息的利用.     

PubMed助手——提高PubMed检索效果的好帮手

介绍一种PubMed辅助软件--PubMed助手,在具备PubMed已有的检索功能基础上,它还增加可视化检索策略编辑器,可以非常容易的编辑复杂的布尔逻辑检索策略,甚至可以根据已有几篇相关文献,自动制定高级检索策略。PubMed助手还提供专门的浏览器,可以直接在主界面中浏览所有检索结果的题录信息,而不用每次都打开新页面。可以突出显示关键字,输出检索结果,点击进入google或Google学术搜索等。     

MEDLINE数据库生物信息学的文献计量分析

对MEDLINE数据库中的生物信息学文献进行计量分析，从而探讨当前生物信息学发展状况。     

基于MEDLINE的非相关文献知识发现

介绍了非相关文献知识发现法的概念及应用情况,以及人机交互系统ARROWSMITH的作用和操作过程;以图示的方式详尽地描述了在MEDLINE数据库中应用ARROWSMITH系统进行非相关文献的知识发现.认为非相关文献知识发现方法为情报研究和情报检索开辟了更广阔的研究领域,为情报学指明了新的研究方向,赋予情报学更加强大的生命力.