24,875 research outputs found

    A survey of orthopaedic journal editors determining the criteria of manuscript selection for publication

    Get PDF
    Background: To investigate the characteristics of editors and criteria used by orthopaedic journal editors in assessing submitted manuscripts. Methods: Between 2008 to 2009 all 70 editors of Medline listed orthopaedic journals were approached prospectively with a questionnaire to determine the criteria used in assessing manuscripts for publication. Results: There was a 42% response rate. There was 1 female editor and the rest were male with 57% greater than 60 years of age. 67% of the editors worked in university teaching hospitals and 90% of publications were in English.The review process differed between journals with 59% using a review proforma, 52% reviewing an anonymised manuscript, 76% using a routine statistical review and 59% of journals used 2 reviewers routinely. In 89% of the editors surveyed, the editor was able to overrule the final decision of the reviewers.Important design factors considered for manuscript acceptance were that the study conclusions were justified (80%), that the statistical analysis was appropriate (76%), that the findings could change practice (72%). The level of evidence (70%) and type of study (62%) were deemed less important. When asked what factors were important in the manuscript influencing acceptance, 73% cited an understandable manuscript, 53% cited a well written manuscript and 50% a thorough literature review as very important factors. Conclusions: The editorial and review process in orthopaedic journals uses different approaches. There may be a risk of language bias among editors of orthopaedic journals with under-representation of non-English publications in the orthopaedic literature

    Interdisciplinarity and research on local issues: evidence from a developing country

    Get PDF
    This paper examines the role of interdisciplinarity on research pertaining to local issues. Using Colombian publications from 1991 until 2011 in the Web of Science, we investigate the relationship between the degree of interdisciplinarity and the local orientation of the articles. We find that a higher degree of interdisciplinarity in a publication is associated with a greater emphasis on local issues. In particular, our results support the view that research that combines cognitively disparate disciplines, what we refer to as distal interdisciplinarity, is associated with more local focus of research. We discuss the policy implications of these results in the context of national research assessments targeting excellence and socio-economic impact

    Interdisciplinarity and research on local issues: evidence from a developing country

    Get PDF
    This paper explores the relationship between interdisciplinarity and research pertaining to local issues. Using Colombian publications from 1991 until 2011 in the Web of Science, we investigate the relationship between the degree of interdisciplinarity and the local orientation of the articles. We find that a higher degree of interdisciplinarity in a publication is associated with a greater emphasis on Colombian issues. In particular, our results suggest that research that combines cognitively disparate disciplines, what we refer to as distal interdisciplinarity, tends to be associated with more local focus of research. We discuss the implications of these results in the context of policies aiming to foster the local socio-economic impact of research in developing countries.Comment: 24 page

    Text mining meets community curation: a newly designed curation platform to improve author experience and participation at WormBase

    Get PDF
    Biological knowledgebases rely on expert biocuration of the research literature to maintain up-to-date collections of data organized in machine-readable form. To enter information into knowledgebases, curators need to follow three steps: (i) identify papers containing relevant data, a process called triaging; (ii) recognize named entities; and (iii) extract and curate data in accordance with the underlying data models. WormBase (WB), the authoritative repository for research data on Caenorhabditis elegans and other nematodes, uses text mining (TM) to semi-automate its curation pipeline. In addition, WB engages its community, via an Author First Pass (AFP) system, to help recognize entities and classify data types in their recently published papers. In this paper, we present a new WB AFP system that combines TM and AFP into a single application to enhance community curation. The system employs string-searching algorithms and statistical methods (e.g. support vector machines (SVMs)) to extract biological entities and classify data types, and it presents the results to authors in a web form where they validate the extracted information, rather than enter it de novo as the previous form required. With this new system, we lessen the burden for authors, while at the same time receive valuable feedback on the performance of our TM tools. The new user interface also links out to specific structured data submission forms, e.g. for phenotype or expression pattern data, giving the authors the opportunity to contribute a more detailed curation that can be incorporated into WB with minimal curator review. Our approach is generalizable and could be applied to additional knowledgebases that would like to engage their user community in assisting with the curation. In the five months succeeding the launch of the new system, the response rate has been comparable with that of the previous AFP version, but the quality and quantity of the data received has greatly improved

    Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

    Full text link
    PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

    Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims

    Get PDF
    Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area
    corecore