Birch Jonathan
Centre for Philosophy of Natural and Social Science, LSE, London, UK
J Med Ethics. 2023 Nov 18. doi: 10.1136/jme-2023-109424.
Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is 'cognitive-motor dissociation' (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient's family, because this information may confuse, alarm and mislead. Instead, we need a procedure for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) A shift from categorical 'responding or not' assessments to degrees of evidence; (2) The use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) The use of standardised probability yardsticks to convey those assessments as clearly as possible.
一些脑损伤患者对外界的言语指令没有明显反应，但却表现出表明其有反应的脑活动模式。这就是“认知-运动分离”（CMD）。最近的研究利用机器学习从脑电图记录中诊断CMD。这些技术的错误发现率很高，引发了一个严重的归纳风险问题。直接将错误发现率告知患者家属并不是解决办法，因为这些信息可能会造成混淆、引起恐慌并产生误导。相反，我们需要一个程序来生成针对具体病例的概率评估，并能够清晰地传达这些评估结果。本文构建了一个可能的程序，该程序包含三个关键要素：（1）从分类的“有反应或无反应”评估转向证据程度评估；（2）使用以患者为中心的先验概率将证据程度转换为概率评估；（3）使用标准化概率标准尽可能清晰地传达这些评估结果。
