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

arXiv: https://arxiv.org/abs/2105.12261 · HTML: https://mlsanity.com/paper/2105.12261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 6 of 6)

- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://mlsanity.com/paper/1806.04185/) (2018)
- [TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection](https://mlsanity.com/paper/2005.04474/) (2020)
- [SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search](https://mlsanity.com/paper/2005.12668/) (2020)
- [Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time](https://mlsanity.com/paper/2005.10865/) (2020)
- [COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research](https://mlsanity.com/paper/2008.07880/) (2020)

## Abstract (arXiv)

The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature. Although semi-structured information resulting from concept recognition and detection of the defining elements of clinical trials (e.g. PICO criteria) has been commonly used to support literature search, the contributions of this abstraction remain poorly understood, especially in relation to text-based retrieval. In this study, we compare the results retrieved by a standard search engine with those filtered using clinically-relevant concepts and their relations. With analysis based on the annotations from the TREC-COVID shared task, we obtain quantitative as well as qualitative insights into characteristics of relational and concept-based literature exploration. Most importantly, we find that the relational concept selection filters the original retrieved collection in a way that decreases the proportion of unjudged documents and increases the precision, which means that the user is likely to be exposed to a larger number of relevant documents.