DT4H calibration compares observed evidence against expected trajectories so the model adapts instead of remaining static.
Evidence → confidence → recalibration → Twin update
DT4H calibration is an adaptive evidence loop. Runtime observations are compared against cohort expectations, reference-human envelopes, and prior Twin trajectories.
New evidence enters from biomarkers, behaviors, outcomes, or clinical events.
Observed state is compared against expected ranges and trajectories.
DT4H computes divergence, confidence shifts, and readiness changes.
The individualized Twin is recalibrated using new evidence.
Updated state becomes available to StateK and SETPOINT.
Repeated consistent signals improve Twin maturity.
Low-signal conditions should not create false certainty.
Calibration identifies instability and triggers reassessment.
Execution outcomes feed back into recalibration logic.
DT4H should represent uncertainty explicitly rather than imply diagnosis.
Runtime systems should expose model maturity and freshness.
SETPOINT outcomes should continuously improve model accuracy.