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The COVID-19 pandemic has driven ever-greater demand for tools which enableefficient exploration of biomedical literature. Although semi-structuredinformation resulting from concept recognition and detection of the definingelements of clinical trials (e.g. PICO criteria) has been commonly used tosupport literature search, the contributions of this abstraction remain poorlyunderstood, especially in relation to text-based retrieval. In this study, wecompare the results retrieved by a standard search engine with those filteredusing clinically-relevant concepts and their relations. With analysis based onthe annotations from the TREC-COVID shared task, we obtain quantitative as wellas qualitative insights into characteristics of relational and concept-basedliterature exploration. Most importantly, we find that the relational conceptselection filters the original retrieved collection in a way that decreases theproportion of unjudged documents and increases the precision, which means thatthe user is likely to be exposed to a larger number of relevant documents.

Impact of detecting clinical trial elements in exploration of COVID-19 literature