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The Article Alert for the week of May 16, 2016 (sample articles)
Büchter RB, Pieper D. Most overviews of Cochrane reviews neglected potential biases from dual authorship. J.Clin.Epidemiol. Epub 2016 Apr 27. PMID: 27131430.
Objective: Some authors of Cochrane overviews have also (co-)authored one or more of the underlying reviews. We examined the extent of dual (co-)authorship in Cochrane overviews, how it is dealt with and whether the issue is raised in protocols.
Study Design: The Cochrane Library was searched for overviews and protocols for overviews in January 2015. Data on dual (co-)authorship was extracted for each review into standard spreadsheets by one author and checked for accuracy by a second.
Results: Twenty overviews and 25 protocols were identified. The overviews included a median of 10 reviews (IQR: 6 to 18.5). In 18/20 overviews (90%) at least one of the included reviews was affected by dual (co-) authorship. A median of 5 (IQR: 2.5 to 7) reviews per overview were affected by dual (co-)authorship. In 8/18 (44%) overviews with dual (co-)authorship, quality assessment was conducted independently. In 7/25 (28%) protocols dual (co-)authorship was mentioned.
Conclusion: Potential biases arising from dual (co-)authorship are often neglected in Cochrane overviews. We argue that authors of Cochrane overviews and Review Groups should pay more attention to the issue, in order to avoid bias and preserve the good reputation that Cochrane overviews will typically deserve.
Copyright © 2016 Elsevier Inc. All rights reserved.
- DOI: http://dx.doi.org/10.1016/j.jclinepi.2016.04.008
- PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27131430
Cortese S. Guidance on conducting systematic reviews/meta-analyses of pharmacoepidemiological studies of safety outcomes: the gap is now filled. Epidemiol.Psychiatr.Sci. Epub 2016 Apr 27. PMID: 27118442.
Until recently, no comprehensive guidance specifically on the conduction of systematic reviews and meta-analyses of pharmacoepidemiological studies of safety outcomes was available. In December 2015, the European Network of Centres for Pharmacoepidemiology and Pharamacovigilance (ENCePP), a network coordinated by the European Medicines Agency, published their 'Guidance on conducting systematic reviews and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes', filling an important gap in the field. This paper highlights the ENCePP recommendations in terms of study identification, data extraction, study quality appraisal and analytical plan. Although the ENCePP document should not be considered as definitive, since it will likely be refined following researchers' feedback, it is expected that it will be highly influential and useful for the field, with the ultimate goal to improve and standardise the conduction and reporting of systematic reviews/meta-analyses of pharmacoepidemiological studies of safety outcomes.
- DOI: http://dx.doi.org/10.1017/S2045796016000299
- PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27118442
Bui DD, Fiol GD, Jonnalagadda S. PDF text classification to leverage information extraction from publication reports. J.Biomed.Inform. Epub 2016 Apr 1. PMID: 27044929.
Objectives: Data extraction from original study reports is a time-consuming, error-prone process in systematic review development. Information extraction (IE) systems have the potential to assist humans in the extraction task, however majority of IE systems were not designed to work on Portable Document Format (PDF) document, an important and common extraction source for systematic review. In a PDF document, narrative content is often mixed with publication metadata or semi-structured text, which add challenges to the underlining natural language processing algorithm. Our goal is to categorize PDF texts for strategic use by IE systems.
Methods: We used an open-source tool to extract raw texts from a PDF document and developed a text classification algorithm that follows a multi-pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE, ABSTRACT, BODYTEXT, SEMISTRUCTURE, and METADATA categories. To validate the algorithm, we developed a gold standard of PDF reports that were included in the development of previous systematic reviews by the Cochrane Collaboration. In a two-step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome mentions.
Results: The multi-pass sieve algorithm achieved an accuracy of 92.6%, which was 9.7% (p<0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm. F-measure improvements were observed in the classification of TITLE (+15.6%), ABSTRACT (+54.2%), BODYTEXT (+3.7%), SEMISTRUCTURE (+34%), and MEDADATA (+14.2%). In addition, use of the algorithm to filter semi-structured texts and publication metadata improved performance of the outcome extraction system (F-measure +4.1%, p=0.002). It also reduced of number of sentences to be processed by 44.9% (p<0.001), which corresponds to a processing time reduction of 50% (p=0.005).
Conclusions: The rule-based multi-pass sieve framework can be used effectively in categorizing texts extracted from PDF documents. Text classification is an important prerequisite step to leverage information extraction from PDF documents.
Copyright © 2016. Published by Elsevier Inc.