Systematic reviews
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Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. ⋯ In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.
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Industry sponsorship has been identified as a factor correlating with positive research findings in several fields of medical science. To date, the influence of industry sponsorship in general and abdominal surgery has not been fully studied. This protocol describes the rationale and planned conduct of a systematic review to determine the association between industry sponsorship and positive outcome in randomised controlled trials in general and abdominal surgery. ⋯ PROSPERO CRD42014010802.
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The purpose of our study is to determine the value and efficacy of searching biomedical databases beyond MEDLINE for systematic reviews. ⋯ Identifying studies for a systematic review where the research is observational is complex. The value each individual study contributes to the review cannot be accurately measured. Consequently, we could not determine the value of results found from searching beyond MEDLINE, Embase, and CINAHL with accuracy. However, hand searching for serendipitous retrieval remains an important aspect due to indexing and keyword challenges inherent in this literature.
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A study-level meta-analysis has shown that proton magnetic resonance spectroscopy is a promising prognostic marker in neonatal hypoxic-ischemic encephalopathy. An individual patient data meta-analysis could yield a prognostic tool with improved accuracy enabling well-founded clinical decisions. ⋯ With less than 40% of the individual patient data available, we refrained from pursuing the proposed study. As future patients may benefit from it, policies mandating data sharing should be introduced.
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Comparative Study
The comparative recall of Google Scholar versus PubMed in identical searches for biomedical systematic reviews: a review of searches used in systematic reviews.
The usefulness of Google Scholar (GS) as a bibliographic database for biomedical systematic review (SR) searching is a subject of current interest and debate in research circles. Recent research has suggested GS might even be used alone in SR searching. This assertion is challenged here by testing whether GS can locate all studies included in 21 previously published SRs. Second, it examines the recall of GS, taking into account the maximum number of items that can be viewed, and tests whether more complete searches created by an information specialist will improve recall compared to the searches used in the 21 published SRs. ⋯ Although its coverage and precision are acceptable, GS, because of its incomplete recall, should not be used as a single source in SR searching. A specialized, curated medical database such as PubMed provides experienced searchers with tools and functionality that help improve recall, and numerous options in order to optimize precision. Searches for SRs should be performed by experienced searchers creating searches that maximize recall for as many databases as deemed necessary by the search expert.