{"publication_id":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","content_hash":"sha256:80166d6f2f84c12b0b33d3c0705b202a46e5274078687a01cceb8ce704fda165","nodes":[{"id":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","type":"publication","title":"Retrieval augmented: MedQA accuracy is the shared direct-receipt signal"},{"id":"claim_1","type":"claim","text":"Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication."},{"id":"claim_2","type":"claim","text":"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?"},{"id":"claim_3","type":"claim","text":"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."},{"id":"source_1","type":"source","study":"Bridging Rationales and Relations: The Graph-Rationale-Guided Retrieval-Augmented Generation in Medical QA","year":2026,"doi":"10.54097/vee3xx26","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_2","type":"source","study":"Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Muti-Agent LLM Framework and Curated Knowledge Databases","year":2026,"doi":"10.1109/ccwc67433.2026.11393764","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_3","type":"source","study":"A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering","year":2024,"doi":"10.1109/bibm62325.2024.10822837","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_4","type":"source","study":"Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions.","year":2025,"doi":"10.1142/9789819807024_0015","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_5","type":"source","study":"Reasoning Over Pre-training: Evaluating LLM Performance and Augmentation in Women's Health","year":2025,"doi":"10.1101/2025.05.22.25328162","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"}],"edges":[{"from":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","to":"claim_1","type":"contains_claim"},{"from":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","to":"claim_2","type":"contains_claim"},{"from":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","to":"claim_3","type":"contains_claim"}],"screening":{"identified":5,"screened":5,"excluded":0,"included":5,"included_or_retained":5,"flow":["identified","screened","excluded_with_reasons","included"],"wording":"5 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]}}