Suppr超能文献

基于对危害的科学分级来制定毒品分类决策:错误前提产生的虚假承诺。

Basing drug scheduling decisions on scientific ranking of harmfulness: false promise from false premises.

机构信息

Carnegie Mellon University Heinz College and Qatar Campus, Pittsburgh, PA 15237, USA.

出版信息

Addiction. 2011 Nov;106(11):1886-90. doi: 10.1111/j.1360-0443.2011.03461.x. Epub 2011 Sep 6.

Abstract

In recent years a number of studies have attempted to rank drugs by a single measure of harmfulness as the basis for decisions about scheduling and classification. These efforts are fundamentally flawed, both conceptually and methodologically. The effort to provide a single measure masks the variety of non-comparable dimensions that are relevant, the fact that benefits are ignored for most, but not all, drugs and that the harms of a drug are not invariant to the policy regime chosen. Methodologically, the most prominent recent effort ignores drug interactions and mixes aggregate and individual harms inappropriately. Instead we suggest that multiple dimensions of harm need to be displayed to inform human judgments of what drugs should be scheduled. Harm is not usefully reducible to a single dimension, and even perfect rankings would not constitute a 'sufficient statistic' for determining scheduling decisions.

摘要

近年来,许多研究试图根据单一的危害性衡量标准对药物进行排名,以此作为制定时间表和分类的决策依据。这些努力从概念和方法论上都存在根本性的缺陷。试图提供单一衡量标准掩盖了各种相关的不可比维度,事实上,对于大多数(但不是所有)药物来说,利益都被忽视了,而且一种药物的危害并不是一成不变的,而是取决于所选择的政策制度。从方法论上讲,最近最引人注目的努力忽视了药物相互作用,并且不恰当地将总体危害和个体危害混合在一起。相反,我们认为需要展示多个危害维度,以便为人类判断哪些药物应该被管制提供信息。危害不能简化为单一维度,即使是完美的排名也不能构成确定管制决策的“充分统计量”。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验