While there have been major advances in documenting the consequences of childhood adversities for psychopathology, Professor Danese's excellent paper challenges existing theory and research methods, urging the field to move ahead with future research that overcomes existing limitations. Importantly, he reminds us of the methodological caveats necessary to consider when assessing the body of evidence for causal effects of childhood trauma and urges caution in interpreting the ACEs literature. This editorial calls attention to and elaborates on a number of issues, including (a) why prospective and retrospective data cannot be used interchangeably; (b) the need for researchers to distinguish among childhood adversities, childhood traumas, and childhood maltreatment; (c) the sparse attention at present to the role of pre-existing vulnerabilities in influencing assessments of the risk of psychopathology; and (d) the critical importance of contextual factors (e.g., age, sex, race, ethnicity, and social class) that are likely to influence the risk of psychopathology. Professor Danese argues for the use of new analytic strategies to advance the field. This editorial elaborates on this recommendation and calls attention to the use of machine learning techniques that may be particularly worthwhile for the child maltreatment field, where there is little psychometric research on measures.

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评论：对儿童创伤研究的更高标准的挑战——对丹麦人的反思（2020）。


尽管在记录童年逆境对精神病理学的影响方面取得了重大进展，但丹尼斯教授的出色论文挑战了现有的理论和研究方法，敦促该领域继续推进克服现有局限性的未来研究。重要的是，他提醒我们在评估儿童创伤因果影响的证据时需要考虑的方法论警告，并敦促谨慎解释 ACE 文献。这篇社论呼吁人们关注并阐述了一些问题，包括（a）为什么前瞻性数据和回顾性数据不能互换使用； (b) 研究人员需要区分童年逆境、童年创伤和儿童虐待； (c) 目前很少关注先前存在的脆弱性在影响精神病理学风险评估方面的作用； (d) 可能影响精神病理学风险的背景因素（例如年龄、性别、种族、民族和社会阶层）至关重要。 Danese 教授主张使用新的分析策略来推进该领域的发展。这篇社论详细阐述了这一建议，并呼吁人们关注机器学习技术的使用，这对于儿童虐待领域可能特别有价值，因为该领域很少有关于措施的心理测量研究。