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《精神疾病诊断与统计手册》第三版、第四版以及《国际疾病分类》第十版作为药物依赖的严重程度量表。

DSM-III, DSM-IV and ICD-10 as severity scales for drug dependence.

作者信息

Langenbucher J W, Morgenstern J, Miller K J

机构信息

Research Diagnostic Project, Rutgers University, Piscataway, NJ 08855-0969, USA.

出版信息

Drug Alcohol Depend. 1995 Aug;39(2):139-50. doi: 10.1016/0376-8716(95)01152-o.

Abstract

The construct of illness severity serves many scientific and clinical functions. This study tested the performance as severity scales of three systems for diagnosing drug dependence--DSM-III, DSM-IV and ICD-10--in a multisite regional sample of 370 clinical subjects. Both lifetime and current severity of four drug problems--alcohol, cannabis, cocaine and opiate dependence--was studied in three stages: (a) item difficulty and internal consistency analysis; (b) probabilistic modeling of distribution behavior; and (c) concurrent validation against a set of independent measures. All three systems, for most drugs correlated with most test variables, had good to excellent concurrent validity. Unexpectedly, DSM-III showed in some instances better item behavior, composite score behavior and concurrent validity than the other systems, though DSM-IV and ICD-10 are based on slimmer generic algorithms, and may represent a good balance between simplicity and concurrent validity. Results suggest that the design of future diagnostic algorithms start at the item level and strive for moderate levels of both internal consistency and difficulty. Composite score distributions can then be modeled in field research, and necessary item corrections can be made before the algorithm is widely promulgated.

摘要

疾病严重程度这一概念具有许多科学和临床功能。本研究在一个由370名临床受试者组成的多地点区域样本中,测试了用于诊断药物依赖的三个系统——《精神疾病诊断与统计手册》第三版(DSM-III)、第四版(DSM-IV)和《国际疾病分类》第十版(ICD-10)——作为严重程度量表的性能。研究了酒精、大麻、可卡因和阿片类药物依赖这四种药物问题的终生严重程度和当前严重程度,分三个阶段进行:(a)项目难度和内部一致性分析;(b)分布行为的概率建模;(c)与一组独立测量方法进行同时效度验证。对于大多数药物,所有这三个系统与大多数测试变量都具有良好到优异的同时效度。出乎意料的是,尽管DSM-IV和ICD-10基于更精简的通用算法,但在某些情况下,DSM-III的项目行为、综合评分行为和同时效度表现优于其他系统,并且可能代表了简单性和同时效度之间的良好平衡。结果表明,未来诊断算法的设计应从项目层面开始,并努力在内部一致性和难度方面达到适度水平。然后可以在现场研究中对综合评分分布进行建模,并在算法广泛发布之前进行必要的项目修正。

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