Evidence map›Paper›PMID 34927002›Full record

ArticleJAMIA open2021

A term-based and citation network-based search system for COVID-19.

Chrysoula Zerva, Samuel Taylor, Axel J Soto, Nhung T H Nguyen, Sophia Ananiadou

Abstract read
In one paragraph

Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Chrysoula ZervaDepartment of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK.
Samuel TaylorDepartment of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK.
Axel J SotoDepartment of Computer Science and Engineering, Universidad Nacional del Sur & Institute for Computer Science and Engineering (ICIC, UNS-CONICET), Bahia Blanca, Argentina.
Nhung T H NguyenDepartment of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK.
Sophia AnaniadouDepartment of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their visualization, accelerating identification of relevant documents. It offers a multi-view interactive search and navigation interface, bringing together unsupervised approaches of term extraction and citation analysis. We conducted a user evaluation with domain experts, including epidemiologists, biochemists, medicinal chemists, and medicine students. In general, most users were satisfied with the relevance and speed of the search results. More interestingly, participants mostly agreed on the capacity of the system to enable exploration and discovery of the search space using the graph visualization and filters. The system is updated on a weekly basis and it is publicly available at http://www.nactem.ac.uk/cord/.

Indexed as

citation networkexploratory search systemsterm extraction

Identifiers

PMID34927002
PMCPMC8672931

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.