Angoff and modified Angoff remain common approaches for setting passing standards, but they can be time-intensive and cognitively demanding for SMEs. This poster presents an alternative hybrid borderline method that combines SME judgment with empirical candidate performance data. SMEs classify representative items by the proficiency level required for success, and those judgments are linked to calibrated item difficulty estimates. A separate candidate survey provides an independent data-driven estimate of borderline proficiency. The final cut score is established by triangulating these sources of evidence. Attendees will learn how this approach can reduce SME burden, improve transparency, and support a scalable, defensible standard-setting process while preserving the central role of expert judgment.