Evidence map›Paper›PMID 28249848›Full record

SynthesisBMJ open2017

Systematic review of the methodological quality of controlled trials evaluating Chinese herbal medicine in patients with rheumatoid arthritis.

Xin Pan, Maria A Lopez-Olivo, Juhee Song, Gregory Pratt, Maria E Suarez-Almazor

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 9% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Goshajinkigan for prevention of chemotherapy-induced peripheral neuropathy: a systematic review and meta-analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2018
    Pooled it
  2. Review
  3. Article
  4. Evidence-based complementary and alternative medicine : eCAM · 2021
    Article
  5. Article
  6. Article
  7. Statistical Accuracy in Rheumatology Research.Mediterranean journal of rheumatology · 2019
    Review
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 at 2 institutions in 2 countries.

Xin PanDepartment of Rheumatology, Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Maria A Lopez-OlivoDepartment of General Internal Medicine, Rheumatology Section, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Juhee SongDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Gregory PrattResearch Medical Library, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Maria E Suarez-AlmazorDepartment of General Internal Medicine, Rheumatology Section, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
The University of Texas MD Anderson Cancer Center · USShanghai University of Traditional Chinese Medicine · CN

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

objectivesWe appraised the methodological and reporting quality of randomised controlled clinical trials (RCTs) evaluating the efficacy and safety of Chinese herbal medicine (CHM) in patients with rheumatoid arthritis (RA).

designFor this systematic review, electronic databases were searched from inception until June 2015. The search was limited to humans and non-case report studies, but was not limited by language, year of publication or type of publication. Two independent reviewers selected RCTs, evaluating CHM in RA (herbals and decoctions). Descriptive statistics were used to report on risk of bias and their adherence to reporting standards. Multivariable logistic regression analysis was performed to determine study characteristics associated with high or unclear risk of bias.

resultsOut of 2342 unique citations, we selected 119 RCTs including 18 919 patients: 10 108 patients received CHM alone and 6550 received one of 11 treatment combinations. A high risk of bias was observed across all domains: 21% had a high risk for selection bias (11% from sequence generation and 30% from allocation concealment), 85% for performance bias, 89% for detection bias, 4% for attrition bias and 40% for reporting bias. In multivariable analysis, fewer authors were associated with selection bias (allocation concealment), performance bias and attrition bias, and earlier year of publication and funding source not reported or disclosed were associated with selection bias (sequence generation). Studies published in non-English language were associated with reporting bias. Poor adherence to recommended reporting standards (<60% of the studies not providing sufficient information) was observed in 11 of the 23 sections evaluated. LIMITATIONS: Study quality and data extraction were performed by one reviewer and cross-checked by a second reviewer. Translation to English was performed by one reviewer in 85% of the included studies.

conclusionsStudies evaluating CHM often fail to meet expected methodological criteria, and high-quality evidence is lacking.

Indexed as

Arthritis, RheumatoidBiasComplementary TherapiesDrugs, Chinese HerbalGuideline AdherenceHumansRandomized Controlled Trials as TopicResearch DesignSelection BiasTreatment OutcomeDrugs, Chinese HerbalChinese herbal medicineQuality of randomized controlled trialsRheumatoid arthritisSystematic reviewTraditional Chinese medicine

Identifiers

PMID28249848
PMCPMC5353312
OpenAlexW2593706484

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.