Evidence map›Paper›PMID 34344385›Full record

ArticleBMC medical informatics and decision making2021

COVID term: a bilingual terminology for COVID-19.

Hetong Ma, Liu Shen, Haixia Sun, Zidu Xu, Li Hou, Sizhu Wu, An Fang, Jiao Li, Qing Qian

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 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

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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

9 authors.

Hetong Ma *Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Liu Shen *Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Haixia Sun *Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Zidu XuInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Li HouInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Sizhu WuInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
An FangInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Jiao LiInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Qing QianInstitute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China. qian.qing@imicams.ac.cn.ORCID 0000-0002-9072-586X

Funding

Chinese Academy of Medical Sciences 2017-I2M-3-014National Key Research and Development Program of China 2016YFC0901901
6 · The paper itself

Abstract

backgroundThe coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination.

methodsWe developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services.

resultsThe proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn ).

conclusionsCOVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application.

Indexed as

COVID-19EpidemicsHumansInformation Storage and RetrievalLanguageSARS-CoV-2BilingualCOVID-19Medical terminologyTerminology system

Identifiers

PMID34344385
PMCPMC8329642

What OpenQuestion holds

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LicenceCC BY
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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.