Toward Credible Wearable Hydrogel Biosensing And Clinical Risk Prediction: Data Quality, Calibration, and External Validity

Abstract

Two distinct lines of inquiry—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—converge on a practical question for wearable hydrogel biosensing and clinical risk prediction: what evidence is needed before a reported advantage becomes a defensible basis for explanation, comparison, or deployment? The review draws on two focal records and 12 established sources already present in the project evidence cache. Its comparative framework links network chemistry, mechanical compliance, and signal stability to downstream questions of clinical features and privacy. Across the evidence base, the decisive issue is alignment: network chemistry shapes what is observed, mechanical compliance shapes how it is compared, and privacy governs whether the conclusion can be transferred. Uncertainty is most informative when reported as part of the result rather than treated as a postscript. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.