bims-librar 2019-03-17 papers

Issue of 2019–03–17
six papers selected by
Thomas Krichel, Open Library Society

Cochrane Database Syst Rev. 2019 Mar 12. 3 MR000041

Search strategies to identify observational studies in MEDLINE and Embase.

Li Li, Helen E Smith, Rifat Atun, Lorainne Tudor Car.

BACKGROUND: Systematic reviews are essential for decision-making. Systematic reviews on observational studies help answer research questions on aetiology, risk, prognosis, and frequency of rare outcomes or complications. However, identifying observational studies as part of systematic reviews efficiently is challenging due to poor and inconsistent indexing in literature databases. Search strategies that include a methodological filter focusing on study design of observational studies might be useful for improving the precision of the search performance.
OBJECTIVES: To assess the sensitivity and precision of a search strategy with a methodological filter to identify observational studies in MEDLINE and Embase.
SEARCH METHODS: We searched MEDLINE (1946 to April 2018), Embase (1974 to April 2018), CINAHL (1937 to April 2018), the Cochrane Library (1992 to April 2018), Google Scholar and Open Grey in April 2018, and scanned reference lists of articles.
SELECTION CRITERIA: Studies using a relative recall approach, i.e. comparing sensitivity or precision of a search strategy containing a methodological filter to identify observational studies in MEDLINE and Embase against a reference standard, or studies that compared two or more methodological filters.
DATA COLLECTION AND ANALYSIS: Two review authors independently screened articles, extracted relevant information and assessed the quality of the search strategies using the InterTASC Information Specialists' Sub-Group (ISSG) Search Filter Appraisal Checklist.
MAIN RESULTS: We identified two eligible studies reporting 18 methodological filters. All methodological filters in these two studies were developed using terms from the reference standard records.The first study evaluated six filters for retrieving observational studies of surgical interventions. The study reported on six filters: one Precision Terms Filter (comprising terms with higher precision while maximum sensitivity was maintained) and one Specificity Terms Filter (comprising terms with higher specificity while maximum sensitivity was maintained), both of which were adapted for MEDLINE, for Embase, and for combined MEDLINE/Embase searches. The study reported one reference standard consisting of 217 articles from one systematic review of which 83.9% of the included studies were case seriesThe second study reported on 12 filters for retrieving comparative non-randomised studies (cNRSs) including cohort, case-control, and cross-sectional studies. This study reported on 12 filters using four different approaches: Fixed method A (comprising of a fixed set of controlled vocabulary (CV) words), Fixed method B (comprising a fixed set of CV words and text words (TW)), Progressive method (CV) (a random choice of study design-related CV terms), and Progressive method (CV or TW) (a random choice of study design-related CV terms, and title and abstracts-based TWs). The study reported four reference standards consisting of 89 cNRSs from four systematic reviews.The six methodological filters developed from the first study reported sensitivity of 99.5% to 100% and precision of 16.7% to 21.1%. The Specificity Terms Filter for combined MEDLINE/Embase was preferred because it had higher precision and equal sensitivity to the Precision Terms Filter. The 12 filters from the second study reported lower sensitivity (48% to 100%) and much lower precision (0.09% to 4.47%). The Progressive method (CV or TW) had the highest sensitivity.There were methodological limitations in both included studies. The first study used one surgical intervention-focused systematic review thus limiting the generalizability of findings. The second study used four systematic reviews but with less than 100 studies. The external validation was performed only on Specificity Terms Filter from the first study Both studies were published 10 years ago and labelling and indexing of observational studies has changed since then.
AUTHORS' CONCLUSIONS: We found 18 methodological filters across two eligible studies. Search strategies from the first study had higher sensitivity and precision, underwent external validation and targeted observational studies. Search strategies from the second study had lower sensitivity and precision, focused on cNRSs, and were not validated externally. Given this limited and heterogeneous evidence, and its methodological limitations, further research and better indexation are needed.

DOI: https://doi.org/10.1002/14651858.MR000041.pub2

Health (London). 2019 Mar 11. 1363459319831331

Where to find accurate information on attention-deficit hyperactivity disorder? A study of scientific distortions among French websites, newspapers, and television programs.

Sébastien Ponnou, Héloïse Haliday, François Gonon.

Attention-deficit/hyperactivity disorder is the most frequent mental disorder among school-age children. This condition has given rise to a large mediatic coverage, which contributed to the shaping of the lay public's perceptions. We therefore conducted two studies on the way attention-deficit/hyperactivity disorder was portrayed in the TV programs and the lay-public press in France between 1995 and 2015, but the growing part played by the Internet required an additional study to analyze and compare the scientific material which is available to the French lay public depending on the source of information used. We studied the 50 first French websites dedicated to attention-deficit/hyperactivity as indexed by Google® search engine using a structured quantitative content analysis for the web. We illustrate our results with excerpts derived from the websites. The conceptions of attention-deficit/hyperactivity disorder available on the Internet are essentially biomedical and comprise an important level of scientific distortion. Findings concerning other mass media such as television programs and the press also demonstrate massive and systematic distortions caused by the role of experts and the pharmaceutical industry. Furthermore, the most consulted media present the highest level of scientific distortions.

Keywords: attention-deficit/hyperactivity disorder; discourse analysis; media

DOI: https://doi.org/10.1177/1363459319831331

Health Inf Manag. 2019 Mar 11. 1833358319831318

Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia.

Michelle A Krahe, Julie Toohey, Malcolm Wolski, Paul A Scuffham, Sheena Reilly.

BACKGROUND:: Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research.
OBJECTIVE:: This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia.
METHOD:: A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle.
RESULTS:: Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams.
CONCLUSION:: Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group.
IMPLICATIONS:: This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.

Keywords: academies and institutes; best practices; data collection; health information management; medical informatics; research

DOI: https://doi.org/10.1177/1833358319831318

J Vis Exp. 2019 Feb 23.

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications.

Dibakar Sigdel, Vincent Kyi, Aiden Zhang, Shaun P Setty, David A Liem, Yu Shi, Xuan Wang, Jiaming Shen, Wei Wang, JiaWei Han, Peipei Ping.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

DOI: https://doi.org/10.3791/59108

IEEE Pulse. 2019 Jan-Feb;10(1):10(1): 18-21

Using Artificial Intelligence to Combat Information Overload in Research.

Douglas Raymond.

Scientists striving for impact in their fields and to develop their own careers must publish papers that represent new and important science, typically in a peer-reviewed journal. The number of scientific articles published has doubled every nine years since WWII, and now stands at more than 3 million peer-reviewed articles annually from more than 34,000 scholarly journals.

DOI: https://doi.org/10.1109/MPULS.2018.2885843