Automated citation searching in systematic review production: A simulation study

Bibliographic aggregators like OpenAlex and Semantic Scholar offer scope for automated citation searching within systematic review production, promising increased efficiency. This study aimed to evaluate the performance of automated citation searching compared to standard search strategies and examine factors that influence performance. Automated citation searching was simulated on 27 systematic reviews across the OpenAlex and Semantic Scholar databases, across three study areas (health, environmental management and social policy). Performance, measured by recall (proportion of relevant articles identified), precision (proportion of relevant articles identified from all articles identified), and F1-F3 scores (weighted average of recall and precision), was compared to the performance of search strategies originally employed by each systematic review. The associations between systematic review study area, number of included articles, number of seed articles, seed article type, study type inclusion criteria, API choice, and performance was analyzed. Automated citation searching outperformed the reference standard in terms of precision (p < 0.05) and F1 score (p < 0.05) but failed to outperform in terms of recall (p < 0.05) and F3 score (p < 0.05). Study area influenced the performance of automated citation searching, with performance being higher within the field of environmental management compared to social policy. Automated citation searching is best used as a supplementary search strategy in systematic review production where recall is more important that precision, due to inferior recall and F3 score. However, observed outperformance in terms of F1 score and precision suggests that automated citation searching could be helpful in contexts where precision is as important as recall.