Analysis of Information on Drug Adverse Reactions Using U.S. Food and Drug Administration Adverse Event Reporting System (FAERS)
Yakugaku Zasshi, ISSN: 1347-5231, Vol: 142, Issue: 4, Page: 341-344
2022
- 2Citations
- 4Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations2
- Citation Indexes2
- Captures4
- Readers4
Review Description
Nowadays, medical big data has been developed and made available in a variety of fields such as epidemiology and pharmacovigilance. Spontaneous reporting databases are one category of medical big data and that has been adequate for analysing events related to side eŠects that rarely occur in general practice. These data are freely available in several countries. In Japan, the Pharmaceuticals and Medical Devices Agency has developed the Japanese Adverse Drug Event Report (JADER), and the Food and Drug Administration (FDA) developed the FDA Adverse Events Reporting System (FAERS) in the United States. Since the release of these medical big data, many researchers in academic and research setting have accessed them, but it is still difficult for many medical professionals to analyse these data due to costs and operation of requisite statistical software. In this section, we give some tips to study spontaneous reporting databases resulting from our learning experiences.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85127457939&origin=inward; http://dx.doi.org/10.1248/yakushi.21-00178-5; http://www.ncbi.nlm.nih.gov/pubmed/35370189; https://www.jstage.jst.go.jp/article/yakushi/142/4/142_21-00178-5/_article/-char/ja/; https://dx.doi.org/10.1248/yakushi.21-00178-5
Pharmaceutical Society of Japan
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