Center for Anxiety and Related Disorders, Boston University, Boston, Massachusetts.
Depress Anxiety. 2014 Nov;31(11):909-11. doi: 10.1002/da.22301.
Over the past several decades, the diagnosis of mental disorders has been characterized by classifying psychopathology into as many discrete diagnoses as can be reliability identified (e.g., APA, 2013). There is increasing evidence, however, that this approach to diagnosis may come at the expense of validity as trivial symptom-level differences are emphasized with little regard for common core mechanisms. Traditionally, cognitive-behavioral (CBT) approaches to treating psychopathology have followed a diagnosis-specific approach such that unique protocols have been developed for most disorders. Recent advances in CBT have suggested that targeting transdiagnostic mechanisms responsible for the development and maintenance of a wider range of psychopathology may be a more efficient approach to treatment than addressing disorder symptoms themselves. In order to create a more personalized treatment package, we propose establishing a profile for each patient that quantifies dysfunction in terms of empirically-supported underlying mechanisms; we further suggest that data from this profile be used to select CBT modules specific to the core mechanisms maintaining an individual patient's symptoms.
在过去的几十年中，精神障碍的诊断方法一直以将精神病理学分类为尽可能多的离散诊断为特征，这些诊断可以可靠地识别出来（例如，APA，2013）。然而，越来越多的证据表明，这种诊断方法可能会以牺牲有效性为代价，因为过于强调微不足道的症状水平差异，而很少考虑常见的核心机制。传统上，认知行为（CBT）治疗精神病理学的方法遵循特定诊断的方法，因此为大多数障碍制定了独特的方案。CBT 的最新进展表明，针对导致更广泛精神病理学发展和维持的跨诊断机制可能是一种比解决障碍症状本身更有效的治疗方法。为了创建更个性化的治疗方案，我们建议为每位患者建立一个档案，根据经验支持的潜在机制来量化功能障碍；我们进一步建议，使用该档案中的数据来选择针对维持个体患者症状的核心机制的 CBT 模块。
