Abstract
Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets—detailing all records screened by humans and their labeling decisions—are imperative. This paper presents the creation of a comprehensive dataset
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