Decision curves are a tool for evaluating the population impact of using a risk model for deciding whether to undergo some intervention, which might be a treatment to help prevent an unwanted clinical event or invasive diagnostic testing such as biopsy. The common formulation of decision curves is based on an opt-in framework. That is, a risk model is evaluated based on the population impact of using the model to opt high-risk patients into treatment in a setting where the standard of care is not to treat. Opt-in decision curves display the population net benefit of the risk model in comparison to the reference policy of treating no patients. In some contexts, however, the standard of care in the absence of a risk model is to treat everyone, and the potential use of the risk model would be to opt low-risk patients out of treatment. Although opt-out settings were discussed in the original decision curve paper, opt-out decision curves are underused. We review the formulation of opt-out decision curves and discuss their advantages for interpretation and inference when treat-all is the standard.