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김시라,왕보람,이정선,김보리,나현오,박영민,최인영,Kim, Sira,Wang, Boram,Lee, Jungsun,Kim, Bori,La, Hyeno,Park, Young Min,Choi, Inyoung 대한임상약리학회 2012 臨床藥理學會誌 Vol.20 No.2
Background: Spontaneous adverse drug reaction (ADR) reporting data has been used for safety of post-market drug surveillance. A system has been required that is able to detect signals associated with drugs by analyzing the collected ADR data. Methods: We developed the web-based automated analysis system (ADR-detector). We used the data which reported ADR spontaneously between March 2009 and December 2010 to Korean Food and Drug Administration. We used 3 statistical indicators for evaluating ADR signals: proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). The ADR reports which were detected as significant signals based on the indicators have been reviewed. Results: Among 153,774 reports, 9,955 cases were related to 4 analgesics which were most frequently reported analgesic drugs during the study period. The numbers of ADR reports associated with each drug are as follow: 5,623 reports in tramadol (56.5 %), 1,720 reports in fentanyl (17.3 %), 1,463 reports in tramadol-combination (14.7 %), and 1,149 reports in ketorolac (11.5 %). Top 5 ADR were nausea (3,351 reports - 33.7 %), vomiting (1,755 reports - 17.6 %), dizziness (1,130 - 11.4 %), rash (412 reports - 4.1 %), and pruritus (354 reports - 3.6 %). 6,674 ADR reports were significant based on PRR and ROR, and 336 reports were significant based on IC. Conclusion: By using the automated analysis system, not only statisticians but also general researchers are able to analyze ADR signals in real-time. Also ADR-detector would provide rapid review and cross-check of ADR.
Su-Jin Lee,Jayoung Park,Yoon Jung Lee,Sira Lee,Woong-Han Kim,Hyun Bae Yoon 한국의학교육학회 2020 Korean journal of medical education Vol.32 No.4
Purpose: The aim of this study was to evaluate the feasibility and satisfaction of an online global health education course for medical students in comparison with an in-person of the course and to assess students’ preferences regarding online methods of delivery. Methods: Second-year medical students enrolled in this course in 2019 (in-person) and 2020 (online). The attendance rate, satisfaction in the course evaluation survey, and academic achievement on the written final examination were utilized to compare the two different methods of course delivery. The medical students who took the online course were also asked about their preferences regarding the method of course delivery and the advantages and drawbacks of each method of online lectures. Results: There was no significant difference in the attendance rate and overall satisfaction between the two groups. The mean score on the written examination of the online course (84.1±19.6) showed comparable effects to the in-person course (78.0±18.3). The percentages of students who achieved high performance (55.5%) and the achieved minimum requirement (95.9%) were also maintained compared to the in-person course (14.6% and 93.6%, respectively). Medical students preferred the online course to the in-person course; in particular, they preferred prerecorded videos over live streaming online lectures. Conclusion: The participation, satisfaction, and the academic achievement of the online course were comparable to those of the in-person course. However, the greatest drawback of the online course was the lack of interaction between peer learners. Therefore, diverse methods for online education should be considered to increase students’ sense of belonging to a learning community.