The development of continuous distribution (CD) proposals for lungs, kidneys, pancreases, and livers display the interrelationship of values and evidence. CD involves identifying attributes that assess progress toward five goals: (1) prioritize sickest candidates first to reduce waitlist deaths; (2) improve long-term survival after transplant; (3) increase transplant opportunities for patients who are medically harder to match; (4) increase transplant opportunities for candidates with distinct characteristics, such as pediatric and prior living donor status; (5) promote efficient management of organ placement through consideration of geographic proximity between donor hospitals and patient transplant centers. Weights are then assigned to the attributes and goals to obtain a composite priority score. Both values and evidence influenced the choice of attributes and their functional forms. Rather than primarily statements of values, weights became design features in machine learning optimization exercises that allowed for the identification of alternatives that predicted the most favorable combinations of efficiency and equity outcomes.