Skip to content

Real-Data Realism Gate

IINTS now separates two questions:

  • Plausibility: does a trace look physiologically plausible?
  • Evidence readiness: is it strong enough for public results, local-AI training, or EUCYS-style claims?

The normal realism validator still returns likely_realistic, needs_review, or likely_unrealistic. The stricter gate in iints.data.realism_governance adds a second layer for research evidence.

Strict Gate

Use review_real_data_realism(report) after validate_realism_dataset(...).

The strict profile blocks traces when:

  • the base verdict is not likely_realistic;
  • any realism check failed;
  • the trace is shorter than 20 hours for daily physiology claims;
  • impossible values, rapid sensor jumps, or long gaps are present;
  • time below 54 mg/dL is too high for a normal-reference trace;
  • meals are present but insulin annotations were lost;
  • no empirical reference profile was used;
  • the reference envelope fails.

Data Evidence Strategy

The SDK registry now ranks real-data sources by evidence use:

  • tier_1_calibration_reference: strongest current candidates for simulator calibration and realism envelopes.
  • tier_2_external_training_candidate: useful after import, MDMP certification, and external validation.
  • tier_3_controlled_validation_candidate: strong independent validation once access is approved.
  • demo_only: good for quickstarts, not final realism claims.

In code:

from iints.data.evidence import rank_real_data_sources

for source in rank_real_data_sources():
    print(source["id"], source["tier"], source["use_case"])

Important Boundary

Passing the strict gate does not mean the SDK is clinically validated. It means the trace is better suited for research evidence than a basic synthetic demo trace.