Evidence map›Paper›PMID 35382101›Full record

ArticleData and information management2020

Translation and Expansion: Enabling Laypeople Access to the COVID-19 Academic Collection.

Daqing He, Zhendong Wang, Khushboo Thaker, Ning Zou

Abstract read
In one paragraph

Article in Data and information management, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daqing HeSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA. USA.
Zhendong WangSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
Khushboo ThakerSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
Ning ZouSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Academic collections, such as COVID-19 Open Research Dataset (CORD-19), contain a large number of scholarly articles regarding COVID-19 and other related viruses. These articles represent the latest development in combating COVID-19 pandemic in various disciplines. However, it is difficult for laypeople to access these articles due to the term mismatch problem caused by their limited medical knowledge. In this article, we present an effort of helping laypeople to access the CORD-19 collection by translating and expanding laypeople's keywords to their corresponding medical terminology using the National Library of Medicine's Consumer Health Vocabulary. We then developed a retrieval system called Search engine for Laypeople to access the COVID-19 literature (SLAC) using open-source software. Utilizing Centers for Disease Control and Prevention's FAQ questions as the basis for developing common questions that laypeople could be interested in, we performed a set of experiments for testing the SLAC system and the translation and expansion (T&E) process. Our experiment results demonstrate that the T&E process indeed helped to overcome the term mismatch problem and mapped laypeople terms to the medical terms in the academic articles. But we also found that not all laypeople's search topics are meaningful to search on the CORD-19 collection. This indicates the scope and the limitation of enabling laypeople to search on academic article collection for obtaining high-quality information.

Indexed as

consumer health vocabularyCOVID-19information retrievallaypeopletranslation and expansion process

Identifiers

PMID35382101
PMCPMC8969476

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.