Structural Medicine v1.2: Case Studies and Data-Driven Interpretation of Structural Lifespan in Integrated Structural Generation Theory (IGS)

This work presents Structural Medicine v1.2, a data-oriented extension of Integrated Structural Generation Theory (IGS), introducing case studies that connect structural variables to disease-like trajectories. While previous versions defined disease as structural transition (v1.0) and introduced the concept of structural lifespan (v1.1), this work focuses on how real-world disease patterns can be interpreted within a unified structural framework. The central idea is: Different diseases are different trajectories of structural distortion. We describe systems using three minimal structural variables:- Structural persistence F(t)- Structural density C(x,t)- Connectivity Γ(t) Using simulated but observation-like data, we show how:- Alzheimer’s disease can be interpreted as a decline in structural persistence F(t)- Cancer can be interpreted as localized structural overgrowth in C(x,t)- Depression can be interpreted as a decline in connectivity Γ(t) These case studies demonstrate how disease progression can be reinterpreted as drift within structural state space rather than as isolated component failure. The framework is minimal and explicitly falsifiable. It predicts that different diseases correspond to distinct trajectories in (F, C, Γ) space, and that these trajectories should be distinguishable in observable data. All figures are fully reproducible, with accompanying Python scripts. This work does not replace empirical data; it reorganizes its meaning, providing a unified structural language for interpreting neurodegeneration, cancer, and network-level disorders.