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SETPOINT Feedback Loop

Animated explanation of how SETPOINT closes the loop between adaptive state, execution, outcomes, and DT4H recalibration.

Feedback loop #

STATEKAdaptive state

Readiness · confidence · trajectory

PROTOCOLExecution logic

Bounded support pathway

PRACTICEUser action

Session · behavior · adherence

OUTCOMEObserved response

Recovery · improvement · fatigue

RECALIBRATIONTwin update

Confidence · trajectory · next state

Why the loop matters #

01

Execution creates evidence

Practices and protocols generate measurable feedback.

02

Outcomes recalibrate

Observed response updates Twin confidence and trajectory.

03

State adapts

StateK changes posture as evidence matures.

04

Governance remains explicit

The loop supports care workflows without replacing clinical judgment.

LayerArchitecture Layer
StatusActive Draft
SystemDT4H / StateK / SETPOINT
BoundaryInfrastructure, not diagnosis
System lineageDT4HTwinStateKSETPOINTOutcomesRecalibration
Infrastructure boundaryDT4H models cohorts, Twins, calibration, and runtime state. It does not diagnose, prescribe, or replace licensed clinical judgment.
Document statusInfrastructure draft
Last updatedMay 2026
Applies toDT4H.ai / AvatarK.ai ecosystem