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Background Patient education is critical to safe and effective postoperative bariatric surgery care. Large language models (LLMs) may improve access to personalized patient education but evidence regarding direct patient use in clinical settings remains limited. We evaluated BariBot, an LLM-powered educational applicat...
High-coverage whole-genome sequencing, large-scale transcriptomics, and eQTL are used to map male cancer cell lines, revealing a region-specific LOY landscape with recurrent euchromatic deletions affecting protein-coding genes and non-coding elements and motivating validation in primary tumor cohorts.
These results suggest that while fine-tuned domain-specific encoders excel at surface-level extraction, larger general-purpose LLMs may be more robust when staging must be inferred from contextual clinical findings.
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