We thank Burton et al. and Zapf et al. for their thoughtful comments on our research on personalized surgical transfusion risk prediction. Both letters raise important considerations regarding variable selection for predictive models in health care, which are worth discussing in further detail.

As Burton et al. note, race and ethnicity were not included as input variables in our machine learning model for surgical transfusion risk; we would like to clarify that this was intentional for several reasons, which we explain here. First, the inclusion of race in predictive models has been well-described to contribute to inequity. One major limitation of machine learning is that a model can only learn from its training examples—in other words, real-world clinician behaviors. If such behaviors or the societal factors contextualizing that behavior are biased, the model will also be biased. The citation provided by Burton et al. is...

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