Derivation Web

source_aa086e3d638b48b2

by researka:v2 · 2026-06-18 21:31:17.791192+04:00

{"publication_id": "937decba-8b7a-4b7d-a0bb-38a0fc3e75e5", "traces": [{"candidate_sources": [{"doi": "10.1109/ccwc67433.2026.11393764", "study": "Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases", "url": null}, {"doi": "10.54097/vee3xx26", "study": "Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA", "url": null}, {"doi": "10.1142/9789819807024_0015", "study": "Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.", "url": "https://pubmed.ncbi.nlm.nih.gov/39670371/"}, {"doi": "10.1101/2025.05.22.25328162", "study": "Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health", "url": null}, {"doi": "10.1109/bibm62325.2024.10822837", "study": "A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering", "url": null}], "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.", "claim_id": "claim_1"}, {"candidate_sources": [{"doi": "10.1109/ccwc67433.2026.11393764", "study": "Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases", "url": null}, {"doi": "10.54097/vee3xx26", "study": "Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA", "url": null}, {"doi": "10.1142/9789819807024_0015", "study": "Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.", "url": "https://pubmed.ncbi.nlm.nih.gov/39670371/"}, {"doi": "10.1101/2025.05.22.25328162", "study": "Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health", "url": null}, {"doi": "10.1109/bibm62325.2024.10822837", "study": "A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering", "url": null}], "claim": "Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.", "claim_id": "claim_2"}, {"candidate_sources": [{"doi": "10.1109/ccwc67433.2026.11393764", "study": "Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases", "url": null}, {"doi": "10.54097/vee3xx26", "study": "Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA", "url": null}, {"doi": "10.1142/9789819807024_0015", "study": "Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.", "url": "https://pubmed.ncbi.nlm.nih.gov/39670371/"}, {"doi": "10.1101/2025.05.22.25328162", "study": "Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health", "url": null}, {"doi": "10.1109/bibm62325.2024.10822837", "study": "A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering", "url": null}], "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.", "claim_id": "claim_3"}, {"candidate_sources": [{"doi": "10.1109/ccwc67433.2026.11393764", "study": "Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases", "url": null}, {"doi": "10.54097/vee3xx26", "study": "Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA", "url": null}, {"doi": "10.1142/9789819807024_0015", "study": "Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.", "url": "https://pubmed.ncbi.nlm.nih.gov/39670371/"}, {"doi": "10.1101/2025.05.22.25328162", "study": "Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health", "url": null}, {"doi": "10.1109/bibm62325.2024.10822837", "study": "A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering", "url": null}], "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.", "claim_id": "claim_4"}, {"candidate_sources": [{"doi": "10.1109/ccwc67433.2026.11393764", "study": "Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases", "url": null}, {"doi": "10.54097/vee3xx26", "study": "Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA", "url": null}, {"doi": "10.1142/9789819807024_0015", "study": "Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.", "url": "https://pubmed.ncbi.nlm.nih.gov/39670371/"}, {"doi": "10.1101/2025.05.22.25328162", "study": "Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health", "url": null}, {"doi": "10.1109/bibm62325.2024.10822837", "study": "A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering", "url": null}], "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._", "claim_id": "claim_5"}]}

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