Abstract
Open access scholarly resources have potential to simplify the literature search process, support more equitable access to research knowledge, and reduce biases from lack of access to relevant literature. OpenAlex is the world's largest open access database of academic research. However, it is not known whether OpenAlex is suitable for comprehensively identifying research for systematic reviews. We present an approach to measure the utility of OpenAlex as part of undertaking a systematic review, and present findings in the context of undertaking a systematic map on the implementation of diabetic eye screening. Procedures were developed to investigate OpenAlex's content coverage and capture, focusing on: (1) availability of relevant research records; (2) retrieval of relevant records from a Boolean search of OpenAlex (3) retrieval of relevant records from combining a PubMed Boolean search with a citations and related-items search of OpenAlex, and (4) efficient estimation of relevant records not identified elsewhere. The searches were conducted in July 2024 and repeated in March 2025 following removal of certain closed access abstracts from the OpenAlex data set. The original systematic review searches yielded 131 relevant records and 128 (98%) of these are present in OpenAlex. OpenAlex Boolean searches retrieved 126 (96%) of the 131 records, and partial screening yielded two relevant records not previously known to the review team. Retrieval was reduced to 123 (94%) when the searches were repeated in March 2025. However, the volume of records from the OpenAlex Boolean search was considerably greater than assessed for the original systematic map. Combining a Boolean search from PubMed and OpenAlex network graph searches yielded 93% recall. It is feasible and useful to investigate the use of OpenAlex as a key information resource for health topics. This approach can be modified to investigate OpenAlex for other systematic reviews. However, the volume of records obtained from searches is larger than that obtained from conventional sources, something that could be reduced using machine learning. Further investigations are needed, and our approach replicated in other reviews.