Discussion of Clinical Workflow Integration and Triage Thresholds
The methodological viability of algorithmic triage in population screening depends heavily on establishing threshold consistency across diverse clinical populations and hardware environments. Evidence from broader oncological screening syntheses demonstrates that machine learning architectures can achieve robust discriminative power, yet translational success requires careful alignment between statistical confidence intervals and operational safety constraints [1]. When applied to screening mammography, the primary clinical imperative is minimizing false-negative triage decisions that could delay critical diagnostic intervention, while still achieving meaningful workload reduction for interpreting radiologists. A protocolized dual-arm extraction framework provides the necessary analytical structure to decouple standalone algorithmic metrics from interactive human-machine diagnostic performance [2]. This structural separation is vital for identifying whether observed variations in cancer detection rates stem from intrinsic model discrimination or differing decision thresholds adopted across clinical settings. Addressing these systematic discrepancies through hierarchical modeling ensures that subsequent empirical pooling accurately reflects operational utility without underestimating the risk of missed lesions in routine practice.