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TRV-2026-0983Certified recordPeer-reviewed

Unmasking bias in the evidence ecosystem: a panoramic analysis of 311,751 meta-analyses using an artificial intelligence agent-based approach

Traditional secondary meta-analysis workflows are highly labour-intensive, time-consuming, and difficult to update in real time. Currently, there is a lack of comprehensive artificial intelligence frameworks capable of automating the entire meta-analysis workflow, including literature screening, data extraction, and quality assessment. Furthermore, a large-scale structured database for systematically analysing the global landscape of published meta-analyses remains unavailable. In this viewpoint, we aimed to eva…

Labor · P Space — documented harm · certified 2026-09-05 · v1 · article view · machine-readable

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Currently, there is a lack of comprehensive artificial intelligence frameworks capable of automating the entire meta-analysis workflow, including literature screening, data extraction, and quality assessment.

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Truvace Impact Record TRV-2026-0983, v1: “Unmasking bias in the evidence ecosystem: a panoramic analysis of 311,751 meta-analyses using an artificial intelligence agent-based approach.” Truvace, 2026-09-05. /record/TRV-2026-0983 (accessed at citation time). sha256 1ba3b91bbf177586…

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