278 validity criterion to assess the accuracy of a model 90. A comprehensive validation of an experimental nocebo model should thus rely on some understanding of “true” or clinical nocebo scores and their correlates within their natural systems, such as in clinical practice and based on, for example, specific clusters and characteristics of chronic pain symptoms. For experimental models to evolve and improve, and for the consistency that we missed in chapters 2 and 3 to be achieved in the field, there is thus a need to obtain clinical markers that can provide a basis for model validations. This is not to say that nocebo experiments should necessarily be performed on clinical populations, but rather, clinical pain scores and nocebo markers can serve to optimize the valuable models on which we can examine nocebo effects with accuracy and precision 89,91. In other words, focusing on the symptomatology of pain patients that are thought to present with negative learned effects on their pain may provide researchers with more accurate representations of potential nocebo magnitudes and impacts outside the laboratory. Potentially unrealistic assumptions are inevitable features of experimental modeling 91, especially so of hypothetical biobehavioral phenomena, but clinical measurements can provide promising avenues forward for building more clinically applicable representations of nocebo hyperalgesia. While few studies have attempted to measure nocebo susceptibility and responding in patient populations 28,29,92, it is imperative to start building on this work. Validating the assumptions of our idealized experimental models in a way that can be applicable across experimental settings holds the potential of combating the inconsistencies and lack of replicability in results, while concurrently encouraging a more valid and accurate platform for understanding nocebo effects and their biobehavioral moderators. Clinical practice is proposed here as a powerful reflective tool for experimental research, a tool to inspect and improve our modeling and understanding of nocebo effects, learned associations, and top-down
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