Scientists Define When a Major Disease Model Should Be Rejected
Researchers have published explicit criteria for falsifying the Universal Resonance Model, a framework that explains how diseases emerge from system instability rather than simple accumulation of damage. The work clarifies how to distinguish between progressive burden and dynamic fragility in patient data—a distinction that affects how companies design biomarker tests and how regulators evaluate therapeutic approaches.
Originaltitel: Under What Biological Conditions Would the Universal Resonance Model Fail?
This paper defines explicit falsification criteria for the Universal Resonance Model (URM), a systems-dynamic framework proposing that disease emergence reflects instability propagation, cross-system coupling, and nonlinear regime transitions. Rather than defending the model, this work specifies the biological conditions under which URM would lose explanatory necessity — including purely monotonic accumulation without instability, absence of cross-domain coupling, deterministic time-locked progression, lack of early-warning signatures, and fully symmetric reversibility. The paper clarifies the distinction between trajectory position markers (e.g., cumulative biomarker burden) and instability markers (e.g., variance amplification, autocorrelation shift, recovery slowing), and positions recent long-horizon biomarker prediction studies as boundary cases for dynamic transition models. The central claim is testable: longitudinal measurement must distinguish cumulative burden from dynamic fragility. This work contributes to methodological rigor in systems medicine by defining where the Universal Resonance Model applies — and where it would fail.