CRP and syringocystadenoma papilliferum: Not only that, this study integrates the prior knowledge provided by LLMs, processed through an embedding layer, with data-driven feature learning in the main network, and dynamically fuses their outputs using a bias network with a gating mechanism, thereby improving the accuracy and interpretability of LLMs in predicting 28-day mortality risk for SCAP patients.<h4>Results</h4>Key predictors of 28-day mortality included inflammatory markers, cytokines, age, CRP, and oxygenation index.