Reframing Wearable Hydrogel Biosensing And Clinical Risk Prediction: Measurement Chains and Validation Design

Abstract

A central challenge in wearable hydrogel biosensing and clinical risk prediction is to compare studies whose mechanisms and validation settings do not share a single denominator. The present review uses dynamic-covalent hydrogel sensing combined with machine learning for Parkinsonian assessment and encrypted interaction and a clinical nomogram and web calculator for individualized lymph-node-metastasis risk as focal cases for a boundary-aware synthesis. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: network chemistry, mechanical compliance, signal stability, clinical features, privacy. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. The combined literature indicates that methodological gains become actionable only when network chemistry and mechanical compliance are evaluated together and when limits associated with privacy are explicit. This shifts the emphasis from isolated scores toward traceable chains of evidence and decision relevance. The contribution is a decision-oriented synthesis that connects method selection to failure cost and treats reproducibility, provenance, and bounded generalization as first-order design requirements.