Evidence map›Paper›PMID 32541482›Full record

ArticleMedicine2020

Effects of Jianpi Yiqi method for chronic obstructive pulmonary disease: A protocol for systematic review and meta-analysis.

Yuxi Wang, Yihui Jiang, Junchen Ge, Zhiwei Zhang, Jia Dong

Abstract 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. 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.

Yuxi WangHangzhou Lin' an TCM Hospital.ORCID 0000-0003-4517-8307
Yihui JiangHangzhou Lin' an TCM Hospital.
Junchen GePeople's Liberation Army No. 962 Hospital, China.
Zhiwei ZhangHangzhou Lin' an TCM Hospital.
Jia DongHangzhou Lin' an TCM Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease with high morbidity and mortality worldwide. Jianpi Yiqi method (JYM) is a classical Chinese therapy in the treatment of COPD. However, there is no systematic review related to JYM for COPD. In this study, we aim to systematically examine the efficacy and safety for clinical use of JYM. METHODS AND ANALYSIS: We will conduct a comprehensive retrieval in the following electronic databases: PubMed, Embase, MEDLINE, Cochrane Library Central Register of Controlled Trials, China National Knowledge Infrastructure (CNKI) database, Wanfang Data Knowledge Service Platform, Chinese Scientific Journals Database (VIP), Chinese Biomedical Literature Service System (SinoMed), and other sources. Two trained reviewers will identify relevant studies, extract data information, and then assess the methodical quality by the Cochrane risk of bias assessment tool, independently. Then the meta-analyses will be conducted by using the RevMan 5.2. Based on the heterogeneity test, data integration is performed using a fixed effect model or a random effects model. A sensitivity analysis will be performed to evaluate the stability of the results. Then publication bias assessment will be conducted by funnel plot analysis and Egger test. Finally, the quality of evidence will be assessed by the GRADE system.

resultsThe results of our research will be published in a peer-reviewed journal.

conclusionThe conclusion of our systematic review will provide evidence to judge whether JYM is an effective intervention for patients with COPD. OSF REGISTRATION NUMBER: 10.17605/OSF.IO/JKQYV.

Indexed as

Medicine, Chinese TraditionalHumansMeta-Analysis as TopicPulmonary Disease, Chronic ObstructiveSystematic Reviews as Topic

Identifiers

PMID32541482
PMCPMC7302594

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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.