The Results Database - Update and Key Issues

Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
New England Journal of Medicine (Impact Factor: 54.42). 03/2011; 364(9):852-60. DOI: 10.1056/NEJMsa1012065
Source: PubMed

ABSTRACT The trial registry was expanded in 2008 to include a database for reporting summary results. We summarize the structure and contents of the results database, provide an update of relevant policies, and show how the data can be used to gain insight into the state of clinical research.
We analyzed data that were publicly available between September 2009 and September 2010.
As of September 27, 2010, received approximately 330 new and 2000 revised registrations each week, along with 30 new and 80 revised results submissions. We characterized the 79,413 registry and 2178 results of trial records available as of September 2010. From a sample cohort of results records, 78 of 150 (52%) had associated publications within 2 years after posting. Of results records available publicly, 20% reported more than two primary outcome measures and 5% reported more than five. Of a sample of 100 registry record outcome measures, 61% lacked specificity in describing the metric used in the planned analysis. In a sample of 700 results records, the mean number of different analysis populations per study group was 2.5 (median, 1; range, 1 to 25). Of these trials, 24% reported results for 90% or less of their participants. provides access to study results not otherwise available to the public. Although the database allows examination of various aspects of ongoing and completed clinical trials, its ultimate usefulness depends on the research community to submit accurate, informative data.

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    • "Analysis and clustering of transcription factor binding site profiles is performed with the use of JASPAR (Bryne et al., 2008), and access to orthology information and clinical trials is given by the ENSEMBL (Vilella et al., 2009) and (Zarin et al., 2011) resources, respectively. Finally, patent information is collected from the EPO Proteins (, "
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    ABSTRACT: The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed® and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. Availability: The service is accessible at:
    Bioinformatics 08/2014; 30(22). DOI:10.1093/bioinformatics/btu524 · 4.62 Impact Factor
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    • "database by the NIH. The creation of this database is itself part of policy initiatives aiming at regulating the controversial domain of clinical research, marred by accusations of conflicts of interest, publication bias, etc. Unsurprisingly, the database itself has run into trouble, due to criticism about its incomplete coverage, failure to include relevant information, and lack of standardization, which in turn has led to additional policy initiatives (compulsory registration of trials if results are to be published, etc.) (Zarin et al. 2011). In spite of all these problems that complicate its appropriation for our own purposes, the database offers the advantage of assembling in a single virtual space entities such as clinical researchers, molecules (drugs), the institutions performing the trial, public organizations (oncology networks), commercial organizations (pharmaceutical and biotech companies), diseases, technologies, and publications. "
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    ABSTRACT: We presently witness a profound transformation of the configuration of biomedical practices, as characterized by an increasingly collective dimension, and by a growing reliance on disruptive technologies that generate large amounts of data. We also witness a proliferation of biomedical databases, often freely accessible on the Web, which can be easily analyzed thanks to network analysis software. In this position paper we discuss how science and technology studies (S&TS) may cope with these developments. In particular, we examine a number of shortcomings of the notion of networks, namely those concerning: (a) the relation between agency and structural analysis; (b) the distinction between network clusters and collectives; (c) the (ac)counting strategies that fuel the networking approach; and (d) the privileged status ascribed to textual documents. This will lead us to reframe the question of the relations between S&TS and biomedical scientists, as big data offer an interesting opportunity for developing new modes of cooperation between the social and the life sciences, while avoiding the dichotomies – between the social and the cognitive, or between texts and practices – that S&TS has successfully managed to discard.
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    • "Although ClinicalTrials .gov is the most established and largest trial registry and includes an estimated 86% of the trials included in the International Clinical Trials Registry Platform by the World Health Organization [50], it did not have complete records for non-interventional studies or phase 0–1 trials, as existing US regulatory requirements do not mandate their registration. Second, the quality of this trial analysis depends on the completion and accuracy of trial information submitted to the registry by the sponsor or their designees [51] [52]. "
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