Evidence map›Paper›PMID 39544735›Full record

ReviewAmerican journal of translational research2024

Traditional Chinese Medicine in post-resection liver cancer therapy: a meta-analysis of efficacy.

Jiasong Wang, Yanli Wang, Ci Song, Shiqiu Dong, Qinming Yu

Abstract readReview
In one paragraph

Review in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Jiasong WangSchool of Basic Medicine, Heilongjiang University of Chinese Medicine Harbin 150000, Heilongjiang, China.
Yanli WangDepartment of Pharmacy, Heilongjiang Nursing College Harbin 150088, Heilongjiang, China.
Ci SongTeaching and Research Office, Heilongjiang Nursing College Harbin 150088, Heilongjiang, China.
Shiqiu DongDepartment of Clinical Medicine, Heilongjiang Nursing College Harbin 150088, Heilongjiang, China.
Qinming YuSchool of Humanities and Management, Heilongjiang University of Chinese Medicine Harbin 150000, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo systematically evaluate the efficacy of traditional Chinese medicine (TCM) in the comprehensive post-resection treatment of primary liver cancer.

methodsClinical studies on TCM in the comprehensive treatment of primary liver cancer post-resection were retrieved from PubMed, Embase, Cochrane and Google Scholar databases. The search encompassed the period from the inception of each database up to June 28, 2024. Two independent reviewers conducted literature screening based on pre-established inclusion and exclusion criteria, performed data extraction, and evaluated the risk of bias and the methodological quality of the included studies. A meta-analysis was conducted utilizing Review Manager 5.3 software.

resultsA total of 12 studies involving 19,116 patients were included in this study. The meta-analysis revealed that TCM treatment following liver cancer resection significantly reduced the recurrence rate, mortality and incidence of adverse events (RR = 0.76, 95% CI 0.70-0.83,

conclusionTCM has a beneficial impact on the comprehensive treatment of primary liver cancer post-resection. However, further validation through higher-quality clinical studies is warranted.

Indexed as

comprehensive treatmenthepatectomymeta-analysisPrimary liver cancertraditional Chinese medicine

Identifiers

PMID39544735
PMCPMC11558418

What OpenQuestion holds

Textmetadata
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.