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Background Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or “pin”) the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction. Methods This is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication). Discussion The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches. Trial registration The trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li. All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion. Electronic supplementary material The online version of this article (doi:10.1186/s13643-016-0373-7) contains supplementary material, which is available to authorized users.

The correct title of the article [1] should be “Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes research: a protocol”.

Background Systematic reviews should provide trustworthy guidance to decision-makers, but their credibility is challenged by the selective reporting of trial results and outcomes. Some trials are not published, and even among clinical trials that are published partially (e.g., as conference abstracts), many are never published in full. Although there are many potential sources of published and unpublished data for systematic reviews, there are no established methods for choosing among multiple reports or data sources about the same trial.

Systematic reviews should provide trustworthy guidance to decision-makers, but their credibility is challenged by the selective reporting of trial results and outcomes. Some trials are not published, and even among clinical trials that are published partially (e.g., as conference abstracts), many are never published in full. Although there are many potential sources of published and unpublished data for systematic reviews, there are no established methods for choosing among multiple reports or data sources about the same trial.We will conduct systematic reviews of the effectiveness and safety of two interventions following the Institute of Medicine (IOM) guidelines: (1) gabapentin for neuropathic pain and (2) quetiapine for bipolar depression. For the review of gabapentin, we will include adult participants with neuropathic pain who do not require ventilator support. For the review of quetiapine, we will include adult participants with acute bipolar depression (excluding mixed or rapid cycling episodes). We will compare these drugs (used alone or in combination with other interventions) with placebo or with the same intervention alone; direct comparisons with other medications will be excluded. For each review, we will conduct highly sensitive electronic searches, and the results of the searches will be assessed by two independent reviewers. Outcomes, study characteristics, and risk of bias ratings will be extracted from multiple reports by two individuals working independently, stored in a publicly available database (Systematic Review Data Repository) and analyzed using commonly available statistical software. In each review, we will conduct a series of meta-analyses using data from different sources to determine how the results are affected by the inclusion of data from multiple published sources (e.g., journal articles and conference abstracts) as well as unpublished aggregate data (e.g., "clinical study reports") and individual participant data (IPD). We will identify patient-centered outcomes in each report and identify differences in the reporting of these outcomes across sources.CRD42015014037 , CRD42015014038.

Background To identify demographic and clinical characteristics associated with cases of hepatosplenic T-cell lymphoma (HSTCL) in patients with Crohn’s disease, and to assess strength of evidence for a causal relationship between medications and HSTCL in Crohn’s disease. Methods We identified cases of HSTCL in Crohn’s disease in studies included in a comparative effectiveness review of Crohn’s disease medications, through a separate search of PubMed and Embase for published case reports, and from the Food and Drug Administration (FDA) Adverse Event Reporting System (AERS). We used three causality assessment tools to evaluate the relationship between medication exposure and HSTCL. Results We found 37 unique cases of HSTCL in patients with Crohn’s disease. Six cases were unique to the published literature and nine were unique to AERS. Cases were typically young (