Researchers developed CAPT, a method that combines a new general-purpose language model with an older clinical model without requiring retraining. The technique uses contrastive decoding to blend clinical knowledge into the newer model while keeping its reasoning abilities intact, and it outperformed existing ensemble methods by 17-41% on clinical tasks. This approach helps hospitals with limited computing resources adopt newer models without the expensive process of retraining them on clinical data.