A data-driven consensus framework for Ct interpretation in real-world multi-assay qPCR diagnostics

While cycle threshold (Ct) values from quantitative PCR (qPCR) serve as the gold-standard indicators of target abundance, their clinical interpretation is frequently confounded by inherent variability across diverse assay designs, reagents, and instrumentation. In this study, we present a data-driven consensus framework for Ct evaluation that uses large-scale, multi-assay amplification data to establish reference patterns of normal Ct behavior. Based on a total of 41,770 amplification curves collected from four routine diagnostic assays across two PCR platforms, we evaluated machine learning models across three experimental scenarios: within-platform validation, cross-assay generalization, and cross-platform transfer. Extreme gradient boosting (XGBoost) achieved the most accurate and stable predictions under data-sufficient, within-platform conditions with a mean absolute error (MAE) of 0.0419, while pooled multi-assay training improved cross-assay robustness compared with single-assay models. Model performance was further assessed using a deviation-based metric to quantify differences between predicted and instrument-reported Ct values, allowing efficient identification of anomalous amplification curves in large datasets. Notably, direct application across platforms without recalibration led to a substantial decline in performance, with a MAE of 2.62, showing platform-dependent variability. These findings indicate strong stability under within-platform and cross-assay conditions, with scalability contingent upon appropriate cross-platform calibration.