An Evidence-Guided Framework for Tissue-Barrier Impedance Review: Deletion and Resistance Sensitivity

Abstract

Reviewing tissue-barrier impedance requires connecting spectral anomalies with resistance estimates and experimental context. We present an evidence-guided framework that coordinates numerical checks and traceable actions for human review. In-house simulations assess the effect of removing flagged frequencies, public datasets test model adaptation, and local measurements demonstrate the review workflow. Removing flagged frequencies reduced resistance error under quadratic objectives, whereas robust fitting showed little average benefit from deletion and a mean error increase for endpoint-pair perturbations. High errors clustered around a few fitted circuit parameter sets, highlighting the value of examining parameter identifiability. Under the tested noise and perturbation conditions, these findings support robust full-spectrum estimation, flag-guided inspection, and matched sensitivity checks.