A meta-model of low back pain to examine collective expert knowledge of treatment effects and their mechanisms

Abstract

Purpose: Low back pain (LBP) is a complex, multifactorial condition with numerous contributors across biopsychosocial domains. To advance understanding of this complexity, we synthesized diverse expert knowledge on treatment effectiveness and underlying mechanisms using a systems-based, collaborative modeling approach. Methods: Twenty-nine experts from diverse disciplines created individual fuzzy cognitive maps (FCMs) to represent their understanding of factors affecting pain, disability, and quality of life (QoL), along with treatment mechanisms. These maps were aggregated into a meta-model comprising 142 Components and 1,161 weighted Connections. Centrality was used to quantify the relative contribution of each domain within the meta-model. Simulations with the meta-model based on expert knowledge (1) estimated the relative effectiveness of treatments on pain, disability, and QoL and (2) identified key Mediators and mediating Domains based on their relative contribution to mediating treatment effects. Results: Psychological, biomechanical, and social/contextual Domains were central to expert conceptualizations of LBP. Simulation indicated cognitive behavioral therapy was considered the most effective among all interventions. Most interventions were mediated by Components across multiple Domains, with psychological factors frequently serving as mediators. The structure of the conceptual meta-model reflected both the multifactorial complexity of LBP and the diversity of expert perspectives regarding factors that influence treatment effectiveness. Conclusion: The developed meta-model provides a novel, systems-based representation of expert knowledge about LBP, enabling quantitative exploration of treatment effects and underlying mechanisms. This conceptual framework also offers a foundation for advancing research on multi-modal, personalized care.