Meeting the Challenge of Scientific Dissemination in the Era of COVID-19: Toward a Modular Approach to Knowledge-Sharing for Radiation Oncology

Fuller, Clifton D.; Dijk, Lisanne V. van; Thompson, Reid F.; Scott, Jacob G.; Ludmir, Ethan B.; Thomas, Charles R.

doi:10.1016/j.ijrobp.2020.06.066

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…4 ). The most representative five articles of topic A 1 mainly focus on public health, social, and educational implications of COVID-19 with different focuses such as “public health law and science” [ 27 ], “public health framework for COVID-19 business liability” [ 28 ], “COVID-19 Response and Education in South Africa” [ 29 , 30 ], and “challenges of scientific dissemination” [ 31 ] ( Appendix B ).…”

Section: Resultsmentioning

confidence: 99%

A critical analysis of COVID-19 research literature: Text mining approach

Zengul¹,

Zengul²,

Mugavero³

et al. 2021

Intelligence-Based Medicine

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Objective Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature. Materials and methods We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21. After the exclusion based on inadequate abstracts, 65,262 articles remained in the final corpus. We utilized natural language processing to curate and generate the term list. We applied topic modeling analyses and multiple correspondence analyses to reveal the major topics and the associations among topics, journal countries, and publication sources. Results In our text mining analyses of NIH's COVID-19 Portfolio, we discovered two sets of eleven major research topics by analyzing abstracts and titles of the articles separately. The eleven major areas of COVID-19 research based on abstracts included the following topics: 1) Public Health, 2) Patient Care & Outcomes, 3) Epidemiologic Modeling, 4) Diagnosis and Complications, 5) Mechanism of Disease, 6) Health System Response, 7) Pandemic Control, 8) Protection/Prevention, 9) Mental/Behavioral Health, 10) Detection/Testing, 11) Treatment Options. Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics. Conclusion By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.

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Section: Resultsmentioning

confidence: 99%

A critical analysis of COVID-19 research literature: Text mining approach

Zengul¹,

Zengul²,

Mugavero³

et al. 2021

Intelligence-Based Medicine

View full text Add to dashboard Cite

show abstract

“…The new trends toward open access also stimulate more modular science dissemination, as proposed by Fuller et al [22] . This concept refers to a system where one can dynamically link modules of a larger research project, such as the research plan, a clinical trial protocol, a pre-print, an open-access publication, the sharing of data, the sharing of software, and a software publication.…”

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confidence: 99%