{"publication_id":"937decba-8b7a-4b7d-a0bb-38a0fc3e75e5","traces":[{"claim_id":"claim_1","claim":"Across 5 independently cited sources, the evidence converges on one bounded claim: rAG-based methods improve accuracy on medical question answering benchmarks (MedQA, MedMCQA, MRCOG) across various base models without task-specific fine-tuning. Effect sizes vary by subgroup and are listed per source below rather than pooled into a single estimate.","candidate_sources":[{"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":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":"https://pubmed.ncbi.nlm.nih.gov/39670371/"},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null}]},{"claim_id":"claim_2","claim":"Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.","candidate_sources":[{"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":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":"https://pubmed.ncbi.nlm.nih.gov/39670371/"},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null}]},{"claim_id":"claim_3","claim":"The surprise is the bounded heterogeneity: the cited direct receipts do not support one uniform effect estimate, so the useful alpha is the specific receipt map and its unresolved spread.","candidate_sources":[{"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":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":"https://pubmed.ncbi.nlm.nih.gov/39670371/"},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null}]},{"claim_id":"claim_4","claim":"Treat this as a receipt map for choosing the next extraction, not as evidence that the topic has one unified effect. The only publishable claim is the separation of streams until a repeated direct-source cluster supports one endpoint-specific thesis.","candidate_sources":[{"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":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":"https://pubmed.ncbi.nlm.nih.gov/39670371/"},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null}]},{"claim_id":"claim_5","claim":"_No direct opposing receipt was selected by this run. Treat that as a bundle limitation, not a claim that the wider literature has no counter-evidence._","candidate_sources":[{"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":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","doi":"10.54097/vee3xx26","url":null},{"study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","doi":"10.1142/9789819807024_0015","url":"https://pubmed.ncbi.nlm.nih.gov/39670371/"},{"study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","doi":"10.1101/2025.05.22.25328162","url":null},{"study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","doi":"10.1109/bibm62325.2024.10822837","url":null}]}]}