Evidence map›Paper›PMID 36292516›Full record

ArticleHealthcare (Basel, Switzerland)2022

The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining.

Ge Zhan, Fuming Yang, Liangbo Zhang, Hanfeng Wang

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. 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

4 authors.

Ge ZhanAI Data Analytics Lab, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU United International College), Zhuhai 519087, China.ORCID 0000-0002-2971-6710
Fuming YangDivision of Science & Technology, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU United International College), Zhuhai 519087, China.
Liangbo ZhangSchool of Economics and Management, Harbin Institute of Technology Shenzhen, Shenzhen 518000, China.
Hanfeng WangDivision of Science & Technology, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU United International College), Zhuhai 519087, China.ORCID 0000-0001-6382-1170

Funding

National Natural Science Foundation of China 71832015
6 · The paper itself

Abstract

We developed a workflow for the search and screening of natural products by drawing from worldwide experiences shared by online platform users, illustrated how to cope with COVID-19 with a text-mining approach, and statistically tested the natural product identified. We built a knowledge base, which consists of three ontologies pertaining to 7653 narratives. Mustard emerged from texting mining and knowledge engineering as an important candidate relating to COVID-19 outcomes. The findings indicate that, after controlling for the containment index, the net import of mustard is related with reduced total and new deaths of COVID-19 for the non-vaccination time period, with considerable effect size (>0.2).

Indexed as

COVID-19knowledge graphnatural foodstext-mining

Identifiers

PMID36292516
PMCPMC9602352

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.