用AI全流程自动化做文献综述和元分析，关键环节需人工把关。
meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis
- 构建10阶段模块化流水线，结合大模型与编程工具完成从检索到成稿的全流程
- 可自动生成论文、检测过度宣称、支持两种方法的网络元分析，成本约15-30美元/次
- 适合研究者快速完成系统评价，但需人工验证结果可靠性
目标：描述meta-pipe这一开源大语言模型（LLM）代理流程的设计架构与逻辑。该流程整合了从文献检索到统计分析、论文撰写及质量保障的完整系统评价与元分析（SR/MA）工作流，并在关键决策点设置强制人工审查。方法：开发了一个包含10个阶段的模块化管道，集成Claude（Anthropic；Opus 4用于推理，Haiku 3.5用于分类）进行文献筛选与信息提取，使用Python（约3,600行代码）实现自动化，R（meta, metafor, gemtc, netmeta）完成统计分析，Quarto用于论文渲染。设定了五个强制人工干预点。我们基于截至2026年3月的公开文档，将meta-pipe的能力与五种现有自动化工具进行了系统比较。结果：meta-pipe具备四项现有单一工具无法提供的功能：基于分析结果自动生成论文、半自动GRADE评估、12种预定义的过度宣称检测模式，以及贝叶斯与频率学派双范式网络元分析。估计单次典型5-10项研究的审查API成本为15-30美元。未报告验证数据；本文为系统描述，非验证研究。结论：在强制人工监督下，端到端的AI辅助证据综合在架构上是可行的。目前正在进行正式验证以复现已发表的Cochrane综述，这对常规使用至关重要。
原文摘要 · Abstract (English)
Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance -- with mandatory human oversight at critical decision points. Study Design and Setting: We developed a 10-stage modular pipeline integrating Claude (Anthropic; Opus 4 for reasoning, Haiku 3.5 for classification) for LLM-assisted screening and extraction, Python (~3,600 lines of code) for automation, R (meta, metafor, gemtc, netmeta) for statistical analysis, and Quarto for manuscript rendering. Five mandatory human decision points enforce oversight. We systematically compared meta-pipe's capabilities with five existing SR automation tools based on published documentation as of March 2026. Results: meta-pipe offers four capabilities not available in any single existing tool: automated manuscript generation from analysis outputs, semi-automated GRADE assessment, overclaim detection (12 predefined patterns), and dual-paradigm network meta-analysis (Bayesian and frequentist). Estimated API cost is $15-30 per typical 5-10 study review. No validation data are reported; this is a system description, not a validation study. Conclusion: End-to-end AI-assisted evidence synthesis is architecturally feasible as an open-source tool with mandatory human oversight. Formal validation reproducing published Cochrane reviews is underway and essential before routine use.
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