Evidence map›Paper›PMID 31871483›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2019

Chinese Herbal Medicine Ameliorated the Development of Chronic Kidney Disease in Patients with Chronic Hepatitis C: A Retrospective Population-Based Cohort Study.

Che-Pin Chang, Yuan-Chih Su, Mei-Chen Lin, Sheng-Teng Huang

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. 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

4 authors at 1 institution in 1 country.

Che-Pin ChangDepartment of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan.
Yuan-Chih SuManagement Office for Health Data, China Medical University Hospital, Taichung, Taiwan.ORCID https://orcid.org/0000-0003-1507-6983
Mei-Chen LinManagement Office for Health Data, China Medical University Hospital, Taichung, Taiwan.
Sheng-Teng HuangDepartment of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan.ORCID https://orcid.org/0000-0002-7495-6115
China Medical University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic kidney disease (CKD) is a serious complication affecting patients with chronic hepatitis. The effectiveness of CHM for the prevention of CKD in hepatitis patients remains unclear. Therefore, we conducted a retrospective cohort study to investigate the effectiveness of CHM in preventing the development of CKD in hepatitis patients. From a subdataset of the Taiwan National Health Insurance Research Database (NHIRD), we included 19,409 patients newly diagnosed with hepatitis B and hepatitis C between the years 2000 and 2010. After exclusion criteria and 1 : 1 propensity score matching process, we compared demographic factors, comorbidities, and correlated drugs between the CHM and non-CHM cohorts. Statistical analysis was applied to evaluate the differences in characteristic distributions and to compare the cumulative incidence of CKD between the CHM and non-CHM cohorts. This study showed that the patients suffering from hepatitis C with CHM treatment more than 90 days as an adjuvant therapy combined with western medical treatment modalities exhibited a decreased risk of developing CKD (hazard ratio (HR) = 0.40, 95% confidence interval (CI) = 0.21-0.76,

Identifiers

PMID31871483
PMCPMC6906860
OpenAlexW2989661057

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.