Runtime intelligence tracks model health, signal freshness, cohort density, reference drift, outcome feedback, and recalibration activity.
New runtime evidence enters the system.
Confidence, maturity, and freshness are evaluated.
Evidence updates state confidence and trajectory.
Current state and readiness are computed.
Execution outcomes return for recalibration.
DT4H runtime is the operational visibility layer for the model. It explains whether the Twin is initialized, calibrated, current, and ready to inform StateK and SETPOINT.
Whether the modeling layer is active and responsive.
How recently new evidence entered the system.
How mature and reliable the individualized model is.
Whether the feedback loop is updating the model.
Whether reference assumptions remain stable.
How execution results flow back into recalibration.
Runtime should identify sparse data rather than overstate confidence.
State should remain provisional when evidence is immature.
Reference assumptions should be reviewed when observed behavior diverges.
SETPOINT feedback should trigger learning rather than repeat stale guidance.
If signals are missing, show low confidence rather than inventing certainty.
Runtime output should show whether state is based on fresh or stale evidence.
SETPOINT execution should feed recalibration rather than remain isolated.