bims-librar Biomed News
on Biomedical librarianship
Issue of 2020–10–25
eightteen papers selected by
Thomas Krichel, Open Library Society
  1. Optimising the process for conducting scoping reviews.
  2. PubMed 2.0.
  3. Database resources of the National Center for Biotechnology Information.
  4. Better synonyms for enriching biomedical search.
  5. UNLV Health Sciences Library's Initial Response to the COVID-19 Pandemic: How a Versatile Environment, Online Technologies, and Liaison Expertise Prepared Library Faculty in Supporting Its User Communities.
  6. Developing a Community of Practice: Building the Research Data Management Librarian Academy.
  7. The clinical informationist as a new member of clinical team.
  8. Utilization of a Librarian in a Pharmacy Laboratory Course.
  9. Popping up in Unexpected Places: Posing a Pop-Up Library to Healthcare Staff.
  10. [New Health Science Descriptors to classify and retrieve information on equity].
  11. Analysis of the quality, reliability, and popularity of information on strabismus on YouTube.
  12. Automatically Assessing Quality of Online Health Articles.
  13. Internet health information education for older adults: A pilot study.
  14. Sources of information in times of health crisis: evidence from Turkey during COVID-19.
  15. Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing.
  16. The Quality, Readability, Completeness, and Accuracy of PTSD Websites for Firefighters.
  17. Topological Similarity Search in Large Combinatorial Fragment Spaces.
  18. Benchmark on Indexing Algorithms for Accelerating Molecular Similarity Search.
  1. BMJ Evid Based Med. 2020 Oct 21. pii: bmjebm-2020-111452. [Epub ahead of print]
    Optimising the process for conducting scoping reviews.
    Colleen Pawliuk, Helen L Brown, Kim Widger, Tammie Dewan, Anne-Mette Hermansen, Marie-Claude Grégoire, Rose Steele, Harold Hal Siden.
      Knowledge synthesis constitutes a key part of evidence-based medicine and a scoping review is a type of knowledge synthesis that maps the breadth of literature on a topic. Conducting a scoping review is resource intensive and, as a result, it can be challenging to maintain best practices throughout the process. Much of the current guidance describes a scoping review framework or broad ways to conduct a scoping review. However, little detailed guidance exists on how to complete each stage to optimise the process. We present five recommendations based on our experience when conducting a particularly challenging scoping review: (1) engage the expertise of a librarian throughout the process, (2) conduct a truly systematic search, (3) facilitate communication and collaboration, (4) explore new tools or repurpose old ones, and (5) test every stage of the process. These recommendations add to the literature by providing specific and detailed advice on each stage of a scoping review. Our intent is for these recommendations to aid other teams that are undertaking knowledge synthesis projects.
    Keywords:  information science
    DOI:  https://doi.org/10.1136/bmjebm-2020-111452
  2. Med Ref Serv Q. 2020 Oct-Dec;39(4):39(4): 382-387
    PubMed 2.0.
    Jacob White.
      After years of strategic planning, the National Library of Medicine has introduced an updated and redesigned version of its PubMed health sciences research website. The new website features a more modern and responsive interface, especially on mobile devices. Tools and features have been relocated to make them more intuitive for new users. While not without some turbulence and slight discomfort for long-time users adjusting to the modernized interface and search engine, the new version of the PubMed website introduced in 2020 succeeds in the website's time-honored task of collecting and making freely accessible high-quality health sciences information and resources.
    Keywords:  National Library of Medicine; PubMed; online databases; review
    DOI:  https://doi.org/10.1080/02763869.2020.1826228
  3. Nucleic Acids Res. 2020 Oct 23. pii: gkaa892. [Epub ahead of print]
    Database resources of the National Center for Biotechnology Information.
    Eric W Sayers, Jeffrey Beck, Evan E Bolton, Devon Bourexis, James R Brister, Kathi Canese, Donald C Comeau, Kathryn Funk, Sunghwan Kim, William Klimke, Aron Marchler-Bauer, Melissa Landrum, Stacy Lathrop, Zhiyong Lu, Thomas L Madden, Nuala O'Leary, Lon Phan, Sanjida H Rangwala, Valerie A Schneider, Yuri Skripchenko, Jiyao Wang, Jian Ye, Barton W Trawick, Kim D Pruitt, Stephen T Sherry.
      The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. The Entrez system provides search and retrieval operations for most of these data from 34 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Custom implementations of the BLAST program provide sequence-based searching of many specialized datasets. New resources released in the past year include a new PubMed interface and NCBI datasets. Additional resources that were updated in the past year include PMC, Bookshelf, Genome Data Viewer, SRA, ClinVar, dbSNP, dbVar, Pathogen Detection, BLAST, Primer-BLAST, IgBLAST, iCn3D and PubChem. All of these resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.
    DOI:  https://doi.org/10.1093/nar/gkaa892
  4. J Am Med Inform Assoc. 2020 Oct 21. pii: ocaa151. [Epub ahead of print]
    Better synonyms for enriching biomedical search.
    Lana Yeganova, Sun Kim, Qingyu Chen, Grigory Balasanov, W John Wilbur, Zhiyong Lu.
       OBJECTIVE: In a biomedical literature search, the link between a query and a document is often not established, because they use different terms to refer to the same concept. Distributional word embeddings are frequently used for detecting related words by computing the cosine similarity between them. However, previous research has not established either the best embedding methods for detecting synonyms among related word pairs or how effective such methods may be.
    MATERIALS AND METHODS: In this study, we first create the BioSearchSyn set, a manually annotated set of synonyms, to assess and compare 3 widely used word-embedding methods (word2vec, fastText, and GloVe) in their ability to detect synonyms among related pairs of words. We demonstrate the shortcomings of the cosine similarity score between word embeddings for this task: the same scores have very different meanings for the different methods. To address the problem, we propose utilizing pool adjacent violators (PAV), an isotonic regression algorithm, to transform a cosine similarity into a probability of 2 words being synonyms.
    RESULTS: Experimental results using the BioSearchSyn set as a gold standard reveal which embedding methods have the best performance in identifying synonym pairs. The BioSearchSyn set also allows converting cosine similarity scores into probabilities, which provides a uniform interpretation of the synonymy score over different methods.
    CONCLUSIONS: We introduced the BioSearchSyn corpus of 1000 term pairs, which allowed us to identify the best embedding method for detecting synonymy for biomedical search. Using the proposed method, we created PubTermVariants2.0: a large, automatically extracted set of synonym pairs that have augmented PubMed searches since the spring of 2019.
    DOI:  https://doi.org/10.1093/jamia/ocaa151
  5. Med Ref Serv Q. 2020 Oct-Dec;39(4):39(4): 344-358
    UNLV Health Sciences Library's Initial Response to the COVID-19 Pandemic: How a Versatile Environment, Online Technologies, and Liaison Expertise Prepared Library Faculty in Supporting Its User Communities.
    Aidy Weeks, Kathryn M Houk, Ruby L Nugent, Mayra Corn, Mellanye Lackey.
      In this case study, the University of Nevada, Las Vegas Health Sciences Library describes how a flexible and technology-focused service model, liaison relationships, and individual expertise all contributed towards rapid mobilization of online instruction, virtual library services, and new resources to keep pace with the sudden needs of their user communities in the School of Medicine, School of Dental Medicine and local Las Vegas community prior to and during stay-at-home mandates related to the COVID-19 global pandemic of 2020.
    Keywords:  3-D printing; COVID-19; case study; collections management; consumer health resources; coronavirus; health sciences library; liaison librarians; online instruction; pandemic; personal protective equipment
    DOI:  https://doi.org/10.1080/02763869.2020.1826197
  6. Med Ref Serv Q. 2020 Oct-Dec;39(4):39(4): 323-333
    Developing a Community of Practice: Building the Research Data Management Librarian Academy.
    Ashley Thomas, Elaine R Martin.
      The Research Data Management Librarian Academy (RDMLA) is a free, online global professional development program designed by librarians for librarians working in research-intensive environments. Developed through a unique partnership that includes a Library and Information Sciences academic program, research and health sciences libraries, and industry, the RDMLA's inception, development, and launch provide helpful insights into the creation of online professional development courses. The RDMLA team's experience building the course's curriculum with an instructional designer (ID) and evaluating the operation and usefulness of the course's content through usability testing provides valuable lessons learned for librarians constructing an online continuing education (CE) course.
    Keywords:  Continuing education; data librarians; online curriculum; research data management
    DOI:  https://doi.org/10.1080/02763869.2020.1826185
  7. J Res Med Sci. 2020 ;25 83
    The clinical informationist as a new member of clinical team.
    Alireza Rahimi, Abolfazl Taheri, Peyman Adibi.
      
    DOI:  https://doi.org/10.4103/jrms.JRMS_578_20
  8. Med Ref Serv Q. 2020 Oct-Dec;39(4):39(4): 334-343
    Utilization of a Librarian in a Pharmacy Laboratory Course.
    Emily P Jones, Jennifer N Wisniewski.
      This report describes utilization of a librarian in a pharmacy laboratory course over two academic years. Library instruction evolved from a simple drug information review session to case-based, hands-on instruction, collaboratively taught with pharmacy faculty. Additionally, LibChat, an online chat service, was piloted in the pharmacy laboratory course so the librarian could be available to students at their point-of-need. Development of the drug information review sessions across both years, student utilization of LibChat, lessons learned, and ideas for improvement for future iterations of the course are described.
    Keywords:  Case-based instruction; drug information; laboratory; librarian; pharmacy
    DOI:  https://doi.org/10.1080/02763869.2020.1826188
  9. Med Ref Serv Q. 2020 Oct-Dec;39(4):39(4): 370-381
    Popping up in Unexpected Places: Posing a Pop-Up Library to Healthcare Staff.
    Dana Gerberi, Julie M Taylor.
      Pop-up libraries have been a trending form of outreach by public and academic libraries during recent years but they are still a novel concept in clinical and hospital settings. Engaging with healthcare staff in common spaces with an inviting temporary display provides an opportunity to proactively raise awareness of library resources and services to non-library users while also piquing interest in a timely topic or special theme. Mayo Clinic librarians describe how a pop-up library was implemented as a unique form of outreach at the Rochester, Minnesota campus in early 2020.
    Keywords:  Pop-up library; outreach
    DOI:  https://doi.org/10.1080/02763869.2020.1826227
  10. Rev Panam Salud Publica. 2020 ;44 e98
    [New Health Science Descriptors to classify and retrieve information on equity].
    Ana Cristina Espíndola Campos, Arthur Treuherz, Renato Toshiyuki Murasaki, Diego Gonzalez, Oscar J Mújica.
      The Health Sciences Descriptors (DeCS) vocabulary establishes a unique and common language that allows the organization and facilitates the search and retrieval of technical and scientific literature on health available in the information sources of the Virtual Health Library. The DeCS, created by the Latin American and Caribbean Center on Health Sciences Information (BIREME), a specialized center of the Pan American Health Organization/World Health Organization (PAHO/WHO), is the translation and extension of the Medical Subject Headings (MeSH) vocabulary, maintained by the United States National Library of Medicine. BIREME, in coordination with experts from Latin America and the Caribbean, has included in the DeCS the topics of equity, gender, ethnicity and human rights-cross-cutting themes in the programmatic framework of PAHO/WHO technical cooperation-to ensure better retrieval and use of scientific information and evidence related to these topics. The objective of this article is to describe the methodology used during the terminology review of the DeCS and to report the results obtained and the impacts of the terminology expansion in the field of equity, which included the inclusion of 35 new descriptors.
    Keywords:  Equity; Medical Subject Headings; access to information; evidence-based practice; information systems
    DOI:  https://doi.org/10.26633/RPSP.2020.98
  11. Strabismus. 2020 Oct 19. 1-6
    Analysis of the quality, reliability, and popularity of information on strabismus on YouTube.
    Mehmet Serhat Mangan, Akin Cakir, Serap Yurttaser Ocak, Hatice Tekcan, Sevcan Balci, Alev Ozcelik Kose.
      To evaluate the quality, reliability, and popularity of YouTube videos addressing strabismus. This is a retrospective, cross-sectional, register-based study. A search was performed using the keywords "strabismus," "squint," "eye squint," and "crossed eye" on YouTube, and the first 50 videos for each keyword were analyzed. The video duration, time since upload, number of total views, view ratio were recorded. Video popularity was recorded using the video power index (VPI). Video educational quality and reliability were measured using the DISCERN questionnaire, Journal of the American Medical Association (JAMA) score, and Global Quality Score (GQS). All videos were also assigned publishers and categories. Among the 200 videos analyzed, 84 were included. The mean duration was 6.2 min, and the mean number of total views was 227,226. The mean VPI score was 189.6 ± 1093.5 (range, 0-11631). The mean DISCERN score, JAMA score, and GQS were 42.2 ± 15.3, 1.9 ± 1.2, and 2.7 ± 1.1, respectively. While the main video publishers were patients (32.1%) and ophthalmologists (28.5%), the main video categories were patient experience (35.7%) and patient information (26.1%). While the most popular videos were uploaded by a TV show/YouTube channel, the videos with the highest educational quality and reliability scores were uploaded by academic institutions. Although some videos contained sufficient information, most of the videos were rated as fair. YouTube users mostly preferred videos that were of low quality. Patients may be receiving biased information, and physicians should be aware of the type of information patients may be accessing on YouTube.
    Keywords:  Strabismus; Youtube; internet; social media
    DOI:  https://doi.org/10.1080/09273972.2020.1836002
  12. IEEE J Biomed Health Inform. 2020 Oct 20. PP
    Automatically Assessing Quality of Online Health Articles.
    Fariha Afsana, Muhammad Ashad Kabir, Naeemul Hassan, Manoranjan Paul.
      Today information in the world wide web is overwhelmed by unprecedented quantity of data on versatile topics with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening.There is currently no generic automated tool for evaluating the quality of online health information spanned over broad range. To address this gap, in this paper, we applied data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labelled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of 84%-90% varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features and assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high quality health articles and thus aiding users in shaping their opinion to make right choice while picking health related help from online.
    DOI:  https://doi.org/10.1109/JBHI.2020.3032479
  13. Geriatr Nurs. 2020 Oct 19. pii: S0197-4572(20)30302-5. [Epub ahead of print]
    Internet health information education for older adults: A pilot study.
    Sun Ju Chang, Eunjin Yang, Kyoung-Eun Lee, Hyunju Ryu.
      This study aimed to develop internet health information education program, and to explore the program's feasibility and preliminary effects. We made use of an intervention mapping approach and adopted as conceptual framework the information-motivation-behavioral skills model to develop the program. We evaluated the feasibility and the impact of the education program using a single-group pretest-posttest design using generalized equation estimation. Eleven older adults participated in the classes from January 25 to February 22, 2019. Each outcome of the behavioral theory-based components of the program-computer/Web knowledge (p < .001), attitude toward internet-based health information (p = .002), eHealth literacy score (p < .001), searching performance scores (p < .001), and level of understanding of internet-based health information (p = .002)-showed significant improvement immediately after the intervention. This pilot study reveals that a behavior theory-based education program for utilizing internet-based health information is an effective way to increase older adults' eHealth literacy.
    Keywords:  Health literacy; Health promotion; Internet access; Older adults; Program development
    DOI:  https://doi.org/10.1016/j.gerinurse.2020.10.002
  14. Z Gesundh Wiss. 2020 Oct 13. 1-7
    Sources of information in times of health crisis: evidence from Turkey during COVID-19.
    Ekmel Geçer, Murat Yıldırım, Ömer Akgül.
       Aim: Media as a source of information can shape public opinion regarding the COVID-19 response. Identifying how and where people seek information during the COVID-19 outbreak is vital to convey the most effective message about managing the COVID-19 crisis. The purpose of this study was to determine the sources of information and investigate the role of various demographic factors-age, gender, educational attainment and perceived economic level-on sources of information.
    Subject and methods: An online survey (n = 4624) was conducted on Turkish public during the early stages of the COVID-19.
    Results: The results showed that internet journalism and social media were the most preferable sources of information. Higher age, educational attainment and economic level were related to higher levels of seeking information from TV, newspaper, internet journalism and informative meetings. Females obtained information more from their friends and family and social media than males. High school graduates or below watched more TV and obtained less information from internet journalism, while university graduates sought information from their families and friends, and postgraduates attended informative meetings and read newspapers. People with medium and high economic status, respectively, watched more TV and read more newspapers, while people with low socioeconomic status attended informative meetings less.
    Conclusion: In sum, this study provides evidence that a source of information might be influenced by demographic factors. Researchers and policymakers can use a source of information to develop crisis-response strategies by considering variations in the demographic factors.
    Keywords:  COVID-19; Coronavirus; Demographic factors; Source of information; Turkey
    DOI:  https://doi.org/10.1007/s10389-020-01393-x
  15. J Informetr. 2020 Nov;14(4): 101091
    Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing.
    Yuzhuo Wang, Chengzhi Zhang.
      In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers can determine popular or classical algorithms in a specific field and help scholars gain a comprehensive understanding of the algorithms and even the field. To this end, this article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field. A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching. The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm. Our results reveal the algorithm with the highest influence in NLP papers and show that classification algorithms represent the largest proportion among the high-impact algorithms. In addition, the evolution of the influence of algorithms reflects the changes in research tasks and topics in the field, and the changes in the influence of different algorithms show different trends. As a preliminary exploration, this paper conducts an analysis of the impact of algorithms mentioned in the academic text, and the results can be used as training data for the automatic extraction of large-scale algorithms in the future. The methodology in this paper is domain-independent and can be applied to other domains.
    Keywords:  Algorithm entity; Full-text content; Influence of algorithms
    DOI:  https://doi.org/10.1016/j.joi.2020.101091
  16. Int J Environ Res Public Health. 2020 Oct 19. pii: E7629. [Epub ahead of print]17(20):
    The Quality, Readability, Completeness, and Accuracy of PTSD Websites for Firefighters.
    Shannon C Killip, Natalie K R Kwong, Joy C MacDermid, Amber J Fletcher, Nicholas R Carleton.
      Firefighters appear at an increased risk for post-traumatic stress disorder (PTSD). Because of PTSD-related stigma, firefighters may search for information online. The current study evaluated the quality, readability, and completeness of PTSD online resources, and to determine how the online treatment recommendations align with current evidence. Google.ca (Canada) searches were performed using four phrases: 'firefighter PTSD', 'firefighter operational stress', 'PTSD symptoms', and 'PTSD treatment'. The 75 websites identified were assessed using quality criteria for consumer health information (DISCERN), readability and health literacy statistics, content analysis, and a comparison of treatments mentioned to the current best evidence. The average DISCERN score was 43.8 out of 75 (indicating 'fair' quality), with 9 'poor' websites (16-30), 31 'fair' websites (31-45), 26 "good" websites (46-60), and nine excellent websites (61-75). The average grade level required to understand the health-related content was 10.6. The most mentioned content was PTSD symptoms (48/75 websites) and PTSD treatments (60/75 websites). The most frequently mentioned treatments were medications (41/75 websites) and cognitive behavioural therapy (40/75 websites). Cognitive behavioural therapy is supported by strong evidence, but evidence for medications appears inconsistent in current systematic reviews. Online PTSD resources exist for firefighters, but the information is challenging to read and lacks evidence-based treatment recommendations.
    Keywords:  firefighters; first responders; health resources; internet; mental health; operational stress injury; posttraumatic stress disorder; public safety personnel; readability; website
    DOI:  https://doi.org/10.3390/ijerph17207629
  17. J Chem Inf Model. 2020 Oct 21.
    Topological Similarity Search in Large Combinatorial Fragment Spaces.
    Louis Bellmann, Patrick Penner, Matthias Rarey.
      In similarity-driven virtual screening, molecular fingerprints are widely used to assess the similarity of all compounds contained in a chemical library to a query compound of interest. This similarity analysis is traditionally done for each member of the library individually. When encoding chemical spaces that surpass billions of compounds in size, it becomes impractical to enumerate all their products, let alone assess their similarity, deeming this approach impossible without investing a substantial amount of resources. In this work, we present a novel search algorithm named SpaceLight for topological fingerprint similarity searching in large, practically non-enumerable combinatorial fragment spaces. In contrast to existing methods, SpaceLight is able to utilize the combinatorial character of these chemical spaces for efficiency while maintaining a high correlation of the description of molecular similarity to well-known molecular fingerprints like ECFP. The resulting software is able to search prominent spaces like EnamineREAL with more than 10 billion compounds in seconds on a standard desktop computer.
    DOI:  https://doi.org/10.1021/acs.jcim.0c00850
  18. J Chem Inf Model. 2020 Oct 23.
    Benchmark on Indexing Algorithms for Accelerating Molecular Similarity Search.
    Chun Jiang Zhu, Minghu Song, Qinqing Liu, Chloé Becquey, Jinbo Bi.
      Structurally similar analogues of given query compounds can be rapidly retrieved from chemical databases by the molecular similarity search approaches. However, the computational cost associated with the exhaustive similarity search of a large compound database will be quite high. Although the latest indexing algorithms can greatly speed up the search process, they cannot be readily applicable to molecular similarity search problems due to the lack of Tanimoto similarity metric implementation. In this paper, we first implement Python or C++ codes to enable the Tanimoto similarity search via several recent indexing algorithms, such as Hnsw and Onng. Moreover, there are increasing interests in computational communities to develop robust benchmarking systems to access the performance of various computational algorithms. Here, we provide a benchmark to evaluate the molecular similarity searching performance of these recent indexing algorithms. To avoid the potential package dependency issues, two separate benchmarks are built based on currently popular container technologies, Docker and Singularity. The Singularity container is a rather new container framework specifically designed for the high-performance computing (HPC) platform and does not need the privileged permissions or the separated daemon process. Both benchmarking methods are extensible to incorporate other new indexing algorithms, benchmarking data sets, and different customized parameter settings. Our results demonstrate that the graph-based methods, such as Hnsw and Onng, consistently achieve the best trade-off between searching effectiveness and searching efficiencies. The source code of the entire benchmark systems can be downloaded from https://github.uconn.edu/mldrugdiscovery/MssBenchmark.
    DOI:  https://doi.org/10.1021/acs.jcim.0c00393
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒22 at 4:18.