食品和药物管理局的算法。特殊的研讨会——监管。

W M Turner
{"title":"食品和药物管理局的算法。特殊的研讨会——监管。","authors":"W M Turner","doi":"10.1177/009286158401800311","DOIUrl":null,"url":null,"abstract":"<p><p>The Food and Drug Administration presently receives and evaluates over 40,000 case reports of adverse drug effects a year. Each report is objectively reviewed and evaluated. A causal association assessment between each drug and reaction is made. The objective causal assessments are based on four basic principles: (a) temporal eligibility, (b) dechallenge and outcome, (c) rechallenge and outcome, and (d) confounding factors. This presentation introduces the algorithm used by the FDA Division of Drug Experience and provides the basic information needed to use the FDA algorithm for making causal relationship assessments.</p>","PeriodicalId":51023,"journal":{"name":"Drug Information Journal","volume":"18 3-4","pages":"259-66"},"PeriodicalIF":0.0000,"publicationDate":"1984-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/009286158401800311","citationCount":"32","resultStr":"{\"title\":\"The Food and Drug Administration algorithm. Special workshop--regulatory.\",\"authors\":\"W M Turner\",\"doi\":\"10.1177/009286158401800311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The Food and Drug Administration presently receives and evaluates over 40,000 case reports of adverse drug effects a year. Each report is objectively reviewed and evaluated. A causal association assessment between each drug and reaction is made. The objective causal assessments are based on four basic principles: (a) temporal eligibility, (b) dechallenge and outcome, (c) rechallenge and outcome, and (d) confounding factors. This presentation introduces the algorithm used by the FDA Division of Drug Experience and provides the basic information needed to use the FDA algorithm for making causal relationship assessments.</p>\",\"PeriodicalId\":51023,\"journal\":{\"name\":\"Drug Information Journal\",\"volume\":\"18 3-4\",\"pages\":\"259-66\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1177/009286158401800311\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drug Information Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/009286158401800311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Information Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/009286158401800311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32

摘要

美国食品和药物管理局目前每年收到并评估4万多例药物不良反应报告。每个报告都被客观地审查和评估。对每种药物与反应之间的因果关系进行评估。客观的因果评估基于四个基本原则:(a)时间资格,(b)取消挑战和结果,(c)重新挑战和结果,以及(d)混杂因素。本报告介绍了FDA药物经验部门使用的算法,并提供了使用FDA算法进行因果关系评估所需的基本信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Food and Drug Administration algorithm. Special workshop--regulatory.

The Food and Drug Administration presently receives and evaluates over 40,000 case reports of adverse drug effects a year. Each report is objectively reviewed and evaluated. A causal association assessment between each drug and reaction is made. The objective causal assessments are based on four basic principles: (a) temporal eligibility, (b) dechallenge and outcome, (c) rechallenge and outcome, and (d) confounding factors. This presentation introduces the algorithm used by the FDA Division of Drug Experience and provides the basic information needed to use the FDA algorithm for making causal relationship assessments.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Drug Information Journal
Drug Information Journal 医学-卫生保健
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊最新文献
Relative Efficiency of Unequal Versus Equal Cluster Sizes for the Nonparametric Weighted Sign Test Estimators in Clustered Binary Data. A Patient Focused Solution for Enrolling Clinical Trials in Rare and Selective Cancer Indications: A Landscape of Haystacks and Needles. Testing in a Prespecified Subgroup and the Intent-to-Treat Population. The Correction of Product Information in Drug References and Medical Textbooks Evaluation of Data Entry Errors and Data Changes to an Electronic Data Capture Clinical Trial Database.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1