Evidence map›Paper›PMID 33157957›Full record

ArticleMedicine2020

Traditional Chinese exercise for COVID-19: A protocol for systematic review and meta-analysis.

Yuanyuan Duan, Mengran Xiong, Heping Wang, Xiaoyan Yao, Henyuan Liu, Guangxi Li

Open access · goldAbstract read
In one paragraph

Article in Medicine, 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
0.3field-weighted citation impact, top 38% of its field
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, 3 citations in OpenAlex.

  1. Article
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

6 authors at 2 institutions in 3 countries.

Yuanyuan DuanGuang 'anmen Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
Mengran Xiong
Heping Wang
Xiaoyan Yao
Henyuan Liu
Apple (Israel) · ILGuang’anmen Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA new type of coronavirus (COVID-19), is spreading all over the world. Under the background of the comprehensive medical treatment and strict prevention and control in China, the number of discharged patients increased substantially. By the end of July, more than 80,000 patients had been cured and discharged from hospital in China. In order to effectively promote the full recovery of the patient's physical and mental functions and quality of life, gradually shift the emphasis of clinical work to convalescence therapy is very important, thus Chinese experts draw up Expert Consensus on Rehabilitation of Chinese Medicine for COVID-19. This systematic review and meta-analysis will assess studies of the effects of traditional Chinese exercise (TCE) for COVID-19 patients. METHODS AND ANALYSIS: We will search 6 English and 4 Chinese databases by 01, December 2020. After a series of screening, Randomized Clinic Trials (RCTs) will be included related to TCE for COVID-19. Two assessors will use the Cochrane bias risk assessment tool to assess the RCTs. Finally, the evidence grade of the results will be evaluated.

resultsThis study will provide a reliable evidence for the selection of TCE therapies for COVID-19.

conclusionThe results of this study will provide references for the selection of TCE treatment for COVID-19, and provide decision making references for clinical research. PROSPERO REGISTRATION NUMBER: CRD42020179095.

Indexed as

Exercise TherapyBetacoronavirusCoronavirus InfectionsCOVID-19HumansMeta-Analysis as TopicPandemicsPneumonia, ViralQigongRandomized Controlled Trials as TopicResearch DesignSARS-CoV-2Systematic Reviews as TopicTai Ji

Identifiers

PMID33157957
PMCPMC7647539
OpenAlexW3096056429

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.