Interactive simulation of longitudinal Twin state evolution, confidence progression, adaptive drift, and recalibration.
Twin trajectory evolves as longitudinal evidence accumulates through clinician input, ArenaK signals, wearables, and SETPOINT execution feedback.
DT4H models progression across time rather than isolated moments.
Trajectory quality changes as adaptive evidence matures.
StateK tracks transitions, instability, and recovery patterns.
SETPOINT practices and outcomes influence future adaptive state.