Prospective Evaluation of Multimodal Parkinson's Disease Risk: A Proposed Observational Protocol for NeuralCipher

Abstract Digital measurements may contribute to Parkinson's disease risk research, but a prospective evaluation must distinguish prediction of future clinical diagnosis from recognition of already established disease. This article proposes an observational protocol for evaluating a frozen multimodal NeuralCipher research model among adults without Parkinson's disease at baseline. It is an unregistered design proposal; no participants have been recruited and no results are reported. The intended design would recruit consecutive eligible adults from participating referral services, obtain a short baseline period of smartphone and wearable measurements, and follow clinical outcomes for 36 months. Recruitment sites, investigators, resources, and ethics arrangements remain to be established. Independent clinicians, blinded to model outputs, would adjudicate baseline eligibility and subsequent diagnoses. The primary outcome would be adjudicated Parkinson's disease diagnosis within 36 months after the prediction landmark, with death treated as a competing event and incomplete follow-up handled explicitly. Evaluation would address probability accuracy, calibration, discrimination, coverage, and incremental value relative to a frozen clinical comparator. A hypothetical planning calculation demonstrates how event frequency and loss to follow-up affect the required recruitment scale; it does not establish a final sample size. The protocol also proposes acquisition safety rules, separate consent choices, controlled data access, missingness analyses, and publication of negative findings. Its purpose is to make an independent validation study concrete enough for methodological and clinical criticism while preserving a clear boundary between a research plan and evidence of clinical usefulness. 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