{"publication_id":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","traces":[{"claim_id":"claim_1","claim":"Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.","candidate_sources":[{"study":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases","doi":"10.1109/ccwc67433.2026.11393764","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":null},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null}]},{"claim_id":"claim_2","claim":"Bounded research question:** Do independent direct receipts on MedQA continue to support a signal on accuracy for the cited systems when comparators are kept explicit?","candidate_sources":[{"study":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases","doi":"10.1109/ccwc67433.2026.11393764","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":null},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null}]},{"claim_id":"claim_3","claim":"Treat this as a benchmark-shaped evidence bundle, not a broad claim about the whole topic. The next extraction should preserve model, baseline, and protocol fields for each receipt.","candidate_sources":[{"study":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases","doi":"10.1109/ccwc67433.2026.11393764","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":null},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null}]}]}