How should closed-model evaluations account for refusals at higher reasoning effort?

A user describes a quirk in evaluating closed models on tasks that can trigger refusals, such as cyber and bio tasks: low reasoning effort produces few refusals and high accuracy, while maximum effort produces many refusals and low accuracy. They ask how to report performance fairly when refusals pull down the maximum-effort score.