bims-librar 2020-11-01 papers

bims-librar

Biomed News

on Biomedical librarianship

Issue of 2020–11–01
nine papers selected by
Thomas Krichel, Open Library Society

The NICE MEDLINE and Embase (Ovid) health apps search filters: development of validated filters to retrieve evidence about health apps.
Literature mining for context-specific molecular relations using multimodal representations (COMMODAR).
fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community.
Medical and health informatics services during and after the COVID-19 pandemic should be virtual, tailored, responsive and interactive: a case study in Belgium.
The Abortion Web Ecosystem: Cross-Sectional Analysis of Trustworthiness and Bias.
YouTube as a source of patient information for Covid-19: An enhanced content-quality and audience engagement analysis.
Analysis of YouTube as a source of information for restless leg syndrome.
Robotic-Assisted Total Knee Arthroplasty: An Assessment of Content, Quality, and Readability of Available Internet Resources.
Internet Information on Oral Cancer Drugs: a Critical Comparison between Website Providers.

Int J Technol Assess Health Care. 2020 Oct 27. 1-7

The NICE MEDLINE and Embase (Ovid) health apps search filters: development of validated filters to retrieve evidence about health apps.

Lynda Ayiku, Thomas Hudson, Sarah Glover, Nicola Walsh, Rachel Adams, Jemma Deane, Amy Finnegan.

   OBJECTIVES: Health apps are software programs that are designed to prevent, diagnose, monitor, or manage conditions. Inconsistent terminology for apps is used in research literature and bibliographic database subject headings. It can therefore be challenging to retrieve evidence about them in literature searches. Information specialists at the United Kingdom's National Institute for Health and Care Excellence (NICE) have developed novel validated search filters to retrieve evidence about apps from MEDLINE and Embase (Ovid).
METHODS: A selection of medical informatics journals was hand searched to identify a "gold standard" (GS) set of references about apps. The GS set was divided into a development and validation set. The filters' search terms were derived from and tested against the development set. An external development set containing app references from published NICE products was also used to inform the development of the filters. The filters were then validated using the validation set. Target recall was >90 percent. The filters' overall recall, specificity, and precision were calculated using all the references identified from the hand search.
RESULTS: Both filters achieved 98.6 percent recall against their validation sets. Overall, the MEDLINE filter had 98.8 percent recall, 71.3 percent specificity, and 22.6 percent precision. The Embase filter had 98.6 percent recall, 74.9 percent specificity, and 24.5 percent precision.
CONCLUSIONS: The NICE health apps search filters retrieve evidence about apps from MEDLINE and Embase with high recall. They can be applied to literature searches to retrieve evidence about the interventions by information professionals, researchers, and clinicians.

Keywords:  Bibliographic; computer-assisted; databases; information science; information storage and retrieval; mobile applications; therapy

DOI:  https://doi.org/10.1017/S026646232000080X
BMC Bioinformatics. 2020 Oct 26. 21(Suppl 5): 250

Literature mining for context-specific molecular relations using multimodal representations (COMMODAR).

Jaehyun Lee, Doheon Lee, Kwang Hyung Lee.

  Biological contextual information helps understand various phenomena occurring in the biological systems consisting of complex molecular relations. The construction of context-specific relational resources vastly relies on laborious manual extraction from unstructured literature. In this paper, we propose COMMODAR, a machine learning-based literature mining framework for context-specific molecular relations using multimodal representations. The main idea of COMMODAR is the feature augmentation by the cooperation of multimodal representations for relation extraction. We leveraged biomedical domain knowledge as well as canonical linguistic information for more comprehensive representations of textual sources. The models based on multiple modalities outperformed those solely based on the linguistic modality. We applied COMMODAR to the 14 million PubMed abstracts and extracted 9214 context-specific molecular relations. All corpora, extracted data, evaluation results, and the implementation code are downloadable at https://github.com/jae-hyun-lee/commodar . CCS CONCEPTS: • Computing methodologies~Information extraction • Computing methodologies~Neural networks • Applied computing~Biological networks.

Keywords:  Biological context; Literature mining; Natural language processing; Representation learning

DOI:  https://doi.org/10.1186/s12859-020-3396-y
Clin Sci (Lond). 2020 Oct 30. 134(20): 2729-2739

fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community.

René Bernard, Tracey L Weissgerber, Evgeny Bobrov, Stacey J Winham, Ulrich Dirnagl, Nico Riedel.

  Statistically significant findings are more likely to be published than non-significant or null findings, leaving scientists and healthcare personnel to make decisions based on distorted scientific evidence. Continuously expanding ´file drawers' of unpublished data from well-designed experiments waste resources creates problems for researchers, the scientific community and the public. There is limited awareness of the negative impact that publication bias and selective reporting have on the scientific literature. Alternative publication formats have recently been introduced that make it easier to publish research that is difficult to publish in traditional peer reviewed journals. These include micropublications, data repositories, data journals, preprints, publishing platforms, and journals focusing on null or neutral results. While these alternative formats have the potential to reduce publication bias, many scientists are unaware that these formats exist and don't know how to use them. Our open source file drawer data liberation effort (fiddle) tool (RRID:SCR_017327 available at: http://s-quest.bihealth.org/fiddle/) is a match-making Shiny app designed to help biomedical researchers to identify the most appropriate publication format for their data. Users can search for a publication format that meets their needs, compare and contrast different publication formats, and find links to publishing platforms. This tool will assist scientists in getting otherwise inaccessible, hidden data out of the file drawer into the scientific community and literature. We briefly highlight essential details that should be included to ensure reporting quality, which will allow others to use and benefit from research published in these new formats.

Keywords:  datasets; null results; publication bias; selective reporting; statistical significance

DOI:  https://doi.org/10.1042/CS20201125
Health Info Libr J. 2020 Oct 28.

Medical and health informatics services during and after the COVID-19 pandemic should be virtual, tailored, responsive and interactive: a case study in Belgium.

Nele S Pauwels, Ann De Meulemeester, Alessia Romagnoli, Heidi Buysse, Renaat Peleman.

  This is part of a new series in this regular feature regarding trends in the provision of information by health science libraries. By sharing expertise and drawing together relevant trends the series intends to serve as a road map for both health science librarians and health informatics professionals. This article shows how a medical and biomedical research library changed practices, and reassessed user needs for the COVID-19 emergency. Discusses changes to online education (and collaborative working) to provide user-friendly services, researcher support tailored to need and re-visioning library space. J.M.

Keywords:  Europe; case reports; data management; education and training; higher education; information literacy; western

DOI:  https://doi.org/10.1111/hir.12330
J Med Internet Res. 2020 Oct 26. 22(10): e20619

The Abortion Web Ecosystem: Cross-Sectional Analysis of Trustworthiness and Bias.

Leo Han, Emily R Boniface, Lisa Yin Han, Jonathan Albright, Nora Doty, Blair G Darney.

   BACKGROUND: People use the internet as a primary source for learning about medical procedures and their associated safety profiles and risks. Although abortion is one of the most common procedures worldwide among women in their reproductive years, it is controversial and highly politicized. Substantial scientific evidence demonstrates that abortion is safe and does not increase a woman's future risk for depressive disorders or infertility. The extent to which information found on the internet reflects these medical facts in a trustworthy and unbiased manner is not known.
OBJECTIVE: The purpose of this study was to collate and describe the trustworthiness and political slant or bias of web-based information about abortion safety and risks of depression and infertility following abortion.
METHODS: We performed a cross-sectional study of internet websites using 3 search topics: (1) is abortion safe?, (2) does abortion cause depression?, and (3) does abortion cause infertility? We used the Google Adwords tool to identify the search terms most associated with those topics and Google's search engine to generate databases of websites related to each topic. We then classified and rated each website in terms of content slant (pro-choice, neutral, anti-choice), clarity of slant (obvious, in-between, or difficult/can't tell), trustworthiness (rating scale of 1-5, 5=most trustworthy), type (forum, feature, scholarly article, resource page, news article, blog, or video), and top-level domain (.com, .net, .org, .edu, .gov, or international domain). We compared website characteristics by search topic (safety, depression, or infertility) using bivariate tests. We summarized trustworthiness using the median and IQR, and we used box-and-whisker plots to visually compare trustworthiness by slant and domain type.
RESULTS: Our search methods yielded a total of 111, 120, and 85 unique sites for safety, depression, and infertility, respectively. Of all the sites (n=316), 57.3% (181/316) were neutral, 35.4% (112/316) were anti-choice, and 7.3% (23/316) were pro-choice. The median trustworthiness score was 2.7 (IQR 1.7-3.7), which did not differ significantly across topics (P=.409). Anti-choice sites were less trustworthy (median score 1.3, IQR 1.0-1.7) than neutral (median score 3.3, IQR 2.7-4.0) and pro-choice (median score 3.7, IQR 3.3-4.3) sites. Anti-choice sites were also more likely to have slant clarity that was "difficult to tell" (41/112, 36.6%) compared with neutral (25/181, 13.8%) or pro-choice (4/23, 17.4%; P<.001) sites. A negative search term used for the topic of safety (eg, "risks") produced sites with lower trustworthiness scores than search terms with the word "safety" (median score 1.7 versus 3.7, respectively; P<.001).
CONCLUSIONS: People seeking information about the safety and potential risks of abortion are likely to encounter a substantial amount of untrustworthy and slanted/biased abortion information. Anti-choice sites are prevalent, often difficult to identify as anti-choice, and less trustworthy than neutral or pro-choice sites. Web searches may lead the public to believe abortion is riskier than it is.

Keywords:  abortion; bias in patient education; infodemic; infodemiology; internet; media; quality of health information; websites

DOI:  https://doi.org/10.2196/20619
Rev Med Virol. 2020 Oct 26.

YouTube as a source of patient information for Covid-19: An enhanced content-quality and audience engagement analysis.

Wajahat Mughal, Zahid Mohammed, Abdullah Zafar, Benyamin Alam.

DOI: https://doi.org/10.1002/rmv.2186
Arq Neuropsiquiatr. 2020 Oct 26. pii: S0004-282X2020005025102. [Epub ahead of print]

Analysis of YouTube as a source of information for restless leg syndrome.

Adalet Arikanoglu, Melike Demir, Mehmet Ufuk Aluclu.

BACKGROUND: YouTube is one of the major resources for health related videos around the world.
OBJECTIVE: The aim of this study was to evaluate the quality of information available on YouTube about restless leg syndrome (RLS).
METHODS: A YouTube search was carried out on https://www.youtube.com for videos pertaining to "restless leg syndrome" by using the keyword "restless leg syndrome". The first 100 relevant videos were included in the study. The videos were accepted as "useful" if they provided scientifically correct information about any aspect of RLS. The videos containing scientifically unproven information are defined as "misleading". The overall quality of all videos was subjectively graded using the global quality scale (GQS), a 5-point Likert scale.
RESULTS: The median video length for the included videos was 3.39 (0.11-85) minutes, and the median views were 6,055 (32-2351490). The median GQS of useful videos was 3 (1-5). The median number of likes and the median number of comments of personal experience videos were significantly higher than that of the useful and misleading videos. Videos uploaded by the university hospitals frequently issued pharmacological treatment of the RLS; however, those uploaded by practitioners, individual users, and TV or social media accounts were about the non-pharmacological treatment of the RLS.
CONCLUSIONS: This study demonstrates that 77% of the videos uploaded on YouTube regarding RLS are in the useful category, whereas only 16 videos were providing misleading information. However, even videos in the useful category do not provide a full and complete description of the RLS.

DOI: https://doi.org/10.1590/0004-282X20200077
J Arthroplasty. 2020 Sep 29. pii: S0883-5403(20)31044-5. [Epub ahead of print]

Robotic-Assisted Total Knee Arthroplasty: An Assessment of Content, Quality, and Readability of Available Internet Resources.

Sean Z Griffiths, Mohamed F Albana, Lauryn D Bianco, Manuel C Pontes, Eddie S Wu.

   BACKGROUND: The use of robotic-assisted total knee arthroplasty (TKA) has significantly increased over the past decade. Internet content is largely unregulated and may contain inaccurate and/or misleading information about robotic TKA. Our goal was to assess the content, quality, and readability of online material regarding robotic-assisted TKA.
METHODS: We conducted an internet search for the top 50 web sites from each of the 3 most popular search engines (Google, Yahoo, and Bing) using the search term robotic total knee replacement. Each web site was assessed for content, quality, and readability. Web site quality was assessed utilizing the QUality Evaluation Scoring Tool (QUEST). Readability was assessed utilizing the Simple Measure of Gobbledygook, Flesch-Kincaid Grade Level, and Flesch Reading Ease Formula scores.
RESULTS: General risks of TKA were discussed in 47.2%, while benefits were discussed in 98.6% of all web sites. Inaccurate claims occurred at a significantly higher rate in physician/community hospital sources compared to university/academic web sites (59% vs 28%, P = .045). Web sites from university/academic web sites had the highest QUEST scores, while physician/community hospital sources scored the lowest (16.1 vs 10.6, P = .01). Most web sites were written at a college reading level or higher.
CONCLUSION: Patients should be counseled on the largely unregulated nature of online information regarding robotic-assisted TKA. Physicians and hospitals should consider revising the readability of their online information to a more appropriate level in order to provide accurate, evidence-based information to allow the patient to make an informed consent decision.

Keywords:  content; quality; readability; robotic total knee replacement; total knee arthroplasty

DOI:  https://doi.org/10.1016/j.arth.2020.09.033
J Cancer Educ. 2020 Oct 30.

Internet Information on Oral Cancer Drugs: a Critical Comparison between Website Providers.

Berit Bartmann, Henriette Schallock, Clara Dubois, Christian Keinki, Bijan Zomorodbakhsch, Michael Hartmann, Jutta Hübner.

  Cancer patients need access to high-quality information, when making decisions about oral cancer drugs. The internet is often used as a source of information published by highly heterogeneous providers. The objective was to evaluate the quality of website providers supplying online information about oral cancer drugs. One hundred websites were analyzed using content-related and formal criteria, selected from three existing evaluation methods used for cancer websites, for medical information (defined by the German Agency for Quality in Medicine), and for the "fact box" tool. A web search by a patient was simulated to identify websites to evaluate. ANOVA was used to assess information provided by non-profit organizations (governmental and non-governmental), online newspapers, for-profit organizations, and private/unknown providers. Content-related quality differences were found between online newspapers and all other categories, with online newspapers ranking significantly lower than for-profit and non-profit websites. As for formal criteria, for-profit providers scored significantly lower than non-profit providers and online newspapers for the aspect of transparency. Internet information on oral cancer drugs published by non-profit organizations constitutes the best available web-based source of information for cancer patients. Health literacy and e-health literacy should be promoted in the public domain to allow patients to reliably apply web-based information. Certification should be required by law to ensure fulfillment of requirements for data reliability and transparency (authorship and funding) before health professionals recommend websites to cancer patients.

Keywords:  E-health literacy; Information quality; Internet; Oral cancer drugs; Transparency

DOI:  https://doi.org/10.1007/s13187-020-01909-9