Search

BackgroundResearchers who perform systematic searches across multiple databases often identify duplicate publications. Identifying such duplicates (“deduplication”) can be extremely time-consuming, but failure to remove these citations can, in the worst instance, lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews.MethodsWe evaluated ASySD’s performance on 5 unseen biomedical systematic search datasets of various sizes (1,845 – 79,880 citations), which had been deduplicated by human reviewers. We compared the performance of ASySD with Endnote’s automated deduplication option and with the Systematic Review Accelerator Deduplication Module (SRA-DM).ResultsASySD identified more duplicates than either SRA-DM or Endnote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of 0.94-0.99. The tool took less than 1 hour to deduplicate all datasets.ConclusionsFor duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.

Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management. Systematic reviews (SRs) can be powerful in summarising and appraising this evidence concerning a specific research question, to highlight areas of improvements, areas for further research and areas where evidence may be sufficient to take forward to other research domains, for instance clinical trial. Guidance and tools for preclinical research synthesis remain limited despite their clear utility. We aimed to create an online end-to-end platform primarily for conducting SRs of preclinical studies, that was flexible enough to support a wide variety of experimental designs, was adaptable to different research questions, would allow users to adopt emerging automated tools and support them during their review process using best practice. In this article, we introduce the Systematic Review Facility (https://syrf.org.uk), which was launched in 2016 and designed to support primarily preclinical SRs from small independent projects to large, crowdsourced projects. We discuss the architecture of the app and its features, including the opportunity to collaborate easily, to efficiently manage projects, to screen and annotate studies for important features (metadata), to extract outcome data into a secure database, and tailor these steps to each project. We introduce how we are working to leverage the use of automation tools and allow the integration of these services to accelerate and automate steps in the systematic review workflow.

BackgroundThe reporting of the measures taken to reduce risks of bias in in vivo research provides important information to research users and represents an important aspect of research quality. The testing of the effectiveness of interventions to improve research quality, and efforts in research improvement, both require the timely and efficient assessment of performance. MethodsWe describe methods which will allow (i) Identification of reports of in vivo research, (ii) retrieval of full text; (iii) attribution of journal, institution, and funder; (iv) automated annotation of the reporting of measures to reduce risks of bias, aggregated by year, journal, institution and funder.DiscussionThis observational study will provide information to guide efforts in research improvement.

The reliability of experimental findings depends on the rigour of experimental design. Here we show limited reporting of measures to reduce the risk of bias in a random sample of life sciences publications, significantly lower reporting of randomisation in work published in journals of high impact, and very limited reporting of measures to reduce the risk of bias in publications from leading United Kingdom institutions. Ascertainment of differences between institutions might serve both as a measure of research quality and as a tool for institutional efforts to improve research quality., A systematic analysis of in vivo research reveals poor reporting of measures that reduce the risk of bias and an inverse relationship between impact factor and the reporting of randomization.