Evidence map›Paper›PMID 39295893›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2020

Fufang Xueshuantong for Diabetic Kidney Disease: A Systematic Review and Meta-Analysis.

Yu-Meng Tan, Jun Hu, Qian Wu, Yi Zhang, Wen-Dong Suo, Yu-Tong Zhou, He Guo, Qing Ni

Abstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 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

8 authors.

Yu-Meng TanDepartment of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Jun HuDepartment of Cardiovascular, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Qian WuBeijing University of Chinese Medicine, Beijing, China.
Yi ZhangBeijing University of Chinese Medicine, Beijing, China.
Wen-Dong SuoBeijing University of Chinese Medicine, Beijing, China.
Yu-Tong ZhouDepartment of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
He GuoDepartment of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Qing NiDepartment of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-4786-4901

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The objective of this meta-analysis was to systematically assess the efficacy and safety of patented Chinese medicine Fufang Xueshuantong (FFXST) for the treatment of diabetic kidney disease (DKD). Methods: Randomized controlled trials (RCTs) of FFXST for DKD treatment were searched until May 31, 2020, in seven electronic databases: PubMed, Embase, Cochrane Library, CNKI, Wanfang, VIP, and Chinese Biomedical Literature. The Cochrane risk test from the Cochrane Handbook was used as a bias tool to assess the methodological quality, and Review Manager (RevMan) 5.3 was used to analyze the results. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria were used to classify the quality of evidence. Results: Thirteen RCTs involving 1,186 patients were included. The meta-analysis revealed that the efficacy of FFXST in treatment of DKD was significantly superior to that of the control treatment ( Conclusion: FFXST appears to be effective in the treatment of DKD. However, the low methodological quality of the RCTs suggests that larger, better-designed RCTs are required to verify the clinical effectiveness and safety of FFXST.

Identifiers

PMID39295893
PMCPMC11410445

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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.