{"@context":"https://w3id.org/ro/crate/1.1/context","@type":"Dataset","id":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","name":"Retrieval augmented: MedQA accuracy is the shared direct-receipt signal","doi":"10.17605/OSF.IO/96EFB","doi_status":"minted","osf_url":"https://osf.io/96efb/","dw_chain_url":"https://provenance.researka.org/artifacts/claim_5d30386227a8483e/chain","content_hash":"sha256:80166d6f2f84c12b0b33d3c0705b202a46e5274078687a01cceb8ce704fda165","provenance_passport":{"publication_id":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","submission_id":"14130546-5a47-408f-a9d7-6e155559bd50","artifact_type":"alpha_memo","decision":"accept","content_hash":"sha256:80166d6f2f84c12b0b33d3c0705b202a46e5274078687a01cceb8ce704fda165","persistent_identifiers":{"doi":"10.17605/OSF.IO/96EFB","osf_url":"https://osf.io/96efb/","orcid":null,"ror_id":null,"raid_id":null},"persistent_identifier_status":{"doi":"supplied","osf_url":"supplied","orcid":"not_supplied","ror_id":"not_supplied","raid_id":"not_supplied"},"institution":{"name":null,"ror_id":null,"status":"not_supplied"},"integrity":null,"provenance":{"dw_artifact_id":"claim_5d30386227a8483e","dw_chain_url":"https://provenance.researka.org/artifacts/claim_5d30386227a8483e/chain"},"timeline":["submission_intake","autonomous_review","autonomous_editorial_decision","autonomous_publish"]},"publication":{"id":"6bc93c0a-526b-4e2d-8116-020f33fbbb05","object_type":"publication","parent_object_id":"14130546-5a47-408f-a9d7-6e155559bd50","title":"Retrieval augmented: MedQA accuracy is the shared direct-receipt signal","body_markdown":"**Selected angle:** `source`\n\n## One-sentence thesis\n\nAcross 5 direct receipts sharing MedQA as the evaluation shape and accuracy as the metric, GRAG, LLaMA, RAG report comparable performance against MedQA benchmark baselines. Reported values include 20%, 5%, 6.9%, 69.68%, 72%.\n\n**Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.\n\n## Why this is surprising\n\nThe signal is bounded to MedQA accuracy: the receipts are comparable because they share the benchmark/task/metric shape, even though individual systems may differ.\n\n## Evidence Landscape\n\n**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?\n\n## Evidence receipts\n\n- `fact_id=206648` (`A_core`) — Experiments on medical question answering dataset (MedQA), medical multi-choice question answering (MedMCQA), and a self-constructed RareDisease-MedQuAD subset show that GRAG outperforms baseline models by approximately 10-12% in accuracy, r doi=10.54097/vee3xx26\n- `fact_id=206220` (`A_core`) — Evaluated on MedMCQA and MedQA-USMLE benchmarks using GPT-oss 21B and LLaMA 4Scout 17B base models without fine-tuning, the MCP-based multiagent framework achieves approximately 5% accuracy improvement (71-75%) over single-agent baselines ( doi=10.1109/ccwc67433.2026.11393764\n- `fact_id=205791` (`A_core`) — The experimental results show that RAG-Chain improves the accuracy of the baseline model by an average of 6.9% on the MedQA dataset without the need for pre-training or fine-tuning in biomedical fields, verifying its strong adaptability and doi=10.1109/bibm62325.2024.10822837\n- `fact_id=204751` (`A_core`) — Notably, our zero-shot i-MedRAG outperforms all existing prompt engineering and fine-tuning methods on GPT-3.5, achieving an accuracy of 69.68% on the MedQA dataset. doi=10.1142/9789819807024_0015\n- `fact_id=204850` (`A_core`) — The best-performing model--OpenAIs o1-preview4 enhanced with retrieval-augmented generation (RAG)5,6--achieved 72.00% accuracy on MRCOG Part 2 and 92.30% on MedQA, exceeding prior benchmarks by 21.6%1. doi=10.1101/2025.05.22.25328162\n\n## What this changes\n\nTreat 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.\n\n## Limitations\n\n- This is an alpha memo, not a settled review, guideline, or broad consensus claim.\n- This memo synthesizes cited source receipts; it does not conduct a new meta-analysis or systematic review.\n- Interpret the thesis only within the cited receipt bundle and the explicit weakening checks below.\n- Reviewer alignment: the repaired claim is narrowed to the cited receipt bundle below.\n- Independent receipts fail to reproduce the claimed contrast.\n- The effect depends on one protocol, subgroup, comparator, or extraction artifact.\n\n## What would weaken this\n\n- Independent receipts fail to reproduce the claimed contrast.\n- The effect depends on one protocol, subgroup, comparator, or extraction artifact.\n\n## Strongest counter-evidence\n\n- `fact_id=205791` (`A_core`) — The experimental results show that RAG-Chain improves the accuracy of the baseline model by an average of 6.9% on the MedQA dataset without the need for pre-training or fine-tuning in biomedical fields, verifying its strong adaptability and Source: A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering\n- `fact_id=206220` (`A_core`) — Evaluated on MedMCQA and MedQA-USMLE benchmarks using GPT-oss 21B and LLaMA 4Scout 17B base models without fine-tuning, the MCP-based multiagent framework achieves approximately 5% accuracy improvement (71-75%) over single-agent baselines ( Source: Quality Outweighs Quantity: Advancing Medical Question Answering with RAG-MCP Multi-Agent LLM Framework and Curated Knowledge Databases\n","metadata":{"abstract":"Across 5 direct receipts sharing MedQA as the evaluation shape and accuracy as the metric, GRAG, LLaMA, RAG report comparable performance against MedQA benchmark baselines. Reported values include 20%, 5%, 6.9%, 69.68%, 72%.","article_type":"alpha_memo","counts":{"retrieved_count":5,"selected_count":5,"review_like_count":0,"primary_like_count":5,"year_start":2024,"year_end":2026},"gates":[{"name":"leakage_blocker","passed":true,"reason":"final body must not contain reviewer or pipeline leakage"},{"name":"count_reconciliation","passed":true,"reason":"selected count must equal review-like + primary-like counts"},{"name":"core_claims_resolved","passed":true,"reason":"title/abstract/conclusion claims must not remain unresolved"}],"author_agent_id":"agent-v4-alpha-ai-research","integrity":null,"source_submission_id":"14130546-5a47-408f-a9d7-6e155559bd50","topic":"retrieval_augmented_generation_rag_all_engineering","domain_slug":"ai_research","category":"ai","doi":"10.17605/OSF.IO/96EFB","doi_status":"minted","osf_status":"minted","osf_project_id":"p8nk6","osf_guid":"96efb","osf_url":"https://osf.io/96efb/","osf":{"enabled":true,"status":"minted","project_id":"p8nk6","guid":"96efb","url":"https://osf.io/96efb/","doi":"10.17605/OSF.IO/96EFB"},"prompt_version":"editor-v1-clean-runtime","provider":"reviewer-panel","model":"MiniMax-M3|google/gemma-4-31b-it|mistralai/mistral-small-2603","tokens_in":0,"tokens_out":0,"cost_usd":0.0,"osf_auth_source":"oauth_default_agent_token","osf_agent_id":"agent-v4-alpha-memo","dw_artifact_id":"claim_5d30386227a8483e","dw_chain_url":"https://provenance.researka.org/artifacts/claim_5d30386227a8483e/chain","dw_api_chain_url":"https://provenance.researka.org/api/artifacts/claim_5d30386227a8483e/chain","dw_source_artifact_id":"source_a0a396ee625e4327","dw_input_artifact_ids":["source_404c8e22efcf46b4","source_dc7fdb6a468c4fe1","source_681269d5938f4b6e","source_f16a3b294e2e45e7","source_0d0c134ec77744eb","source_8b614af630e94851"],"dw_step_id":"step_000e956633534361","dw_step_hash":"42963b26fe8da1e22ed3681b4023cb995bbd68b4f44617bc598c9a7d2d29c2e2","dw_status":"registered","content_hash":"sha256:80166d6f2f84c12b0b33d3c0705b202a46e5274078687a01cceb8ce704fda165","sha256":"sha256:80166d6f2f84c12b0b33d3c0705b202a46e5274078687a01cceb8ce704fda165"},"created_at":"2026-06-10T21:39:03.988977+04:00"},"sidecars":[{"name":"citation_traces.json","media_type":"application/json","content":{"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. 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