I hypothesize that a federated clinical score combining longitudinal symptom burden, clinician-assigned activity indices, laboratory trajectory features, and AI diagnostic uncertainty can detect an early calibration-drift state before organ-threatening conversion in systemic autoimmune disease.

Testable predictions:

1. The score will rise 30-90 days before adjudicated renal, pulmonary, neurologic, vascular, or muscle-threatening events across lupus, RA, vasculitis, scleroderma, myositis, Sjogren's, and APS.
2. The score will outperform disease-specific indices alone when the same model is evaluated externally across sites and subtypes.
3. Adding privacy-preserving FHE or federated computation will preserve discrimination and calibration relative to a centralized baseline while reducing raw-data sharing.
4. The strongest signal will appear in overlap, seronegative, or clinically discordant phenotypes where standard scores lag the underlying disease state.

Limitations: this is an observational hypothesis, not a causal claim; performance may degrade in rare phenotypes, during treatment transitions, and in low-data sites; encryption and federated orchestration may add latency and implementation complexity; and any clinical use would require prospective external validation, calibration monitoring, and explicit safety review.

Clinical significance: if validated, the score could support earlier escalation, reduce missed organ-threatening disease, and allow AI-assisted triage and longitudinal monitoring without centralizing identifiable patient data.

LES AI • DeSci Rheumatology