Given a passage with a question and an answer to that question, classify if the answer actually answers the question into 'yes' or 'no'. Output 'yes' if the answer answers the question and output 'no' if the answer does not answer the question.

Ex Input:
Question: Does dietary beet pulp decrease taurine status in dogs fed low protein diet?
Answer: These results do not support the hypothesis that RB is likely to be a primary cause of lamb meal and rice diets, increasing the risk of taurine deficiency in large dogs. However these indicate that BP may contribute to a decrease taurine status in dogs by increasing excretion of fecal BA and decreasing protein digestibility, thus decreasing the bioavailability of sulfur amino acids, the precursors of taurine.

Ex Output:
yes


Ex Input:
Question: Does positive expression of miR-361-5p indicate better prognosis for breast cancer patients?
Answer: These results indicated that miR-361-5p expression is an independent predictive factor for better prognosis in BC.

Ex Output:
yes


Ex Input:
Question: Do logic models help make sense of complexity in systematic reviews and health technology assessments?
Answer: Logic models can help authors of systematic reviews and HTAs to explicitly address and make sense of complexity, adding value by achieving a better understanding of the interactions between the intervention, its implementation, and its multiple outcomes among a given population and context. They thus have the potential to help build systematic review capacity-in SSA and other LMICs-at an individual level, by equipping authors with a tool that facilitates the review process; and at a system-level, by improving communication between producers and potential users of research evidence.

Ex Output:

yes