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Reducing clinical label disagreement by 36% reveals how LLMs can transform rare-disease modeling by embedding expert knowledge directly into representation learning.
By embedding selection mechanisms into the simulation process, this framework transforms the challenge of selection bias into a manageable simulation problem, yielding more accurate parameter estimates.
