Evidence map›Paper›PMID 35875080›Full record

SynthesisFrontiers in oncology2022

Exploring the Pharmacological Mechanism of the Effective Chinese Medicines Against Gynecological Cancer Based on Meta-Analysis Combined With Network Pharmacology Analysis.

Ning Ren, Lulin Yu, Lihui Qian, Gewei Ye, Zhenzheng Zhu, Jieru Yu, Leitao Sun, Leyin Zhang

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 26% 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, 5 citations in OpenAlex.

  1. Article
  2. Frontiers in nutrition · 2025
    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

8 authors at 2 institutions in 1 country.

Ning RenHangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China.
Lulin YuThe First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Lihui QianThe First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Gewei YeThe First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Zhenzheng ZhuThe First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Jieru YuSchool of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
Leitao SunDepartment of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.
Leyin ZhangDepartment of Oncology, Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China.
Zhejiang Chinese Medical University · CNHangzhou Hospital of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This meta-analysis plus network pharmacology aimed to investigate whether traditional Chinese medicine (TCM) combined with chemotherapy is associated with more beneficial efficacy data in the treatment of gynecological cancer (GC). A total of 11 randomized controlled trials (RCTs) consisting of 863 GC patients were included. Results showed a better ORR (RR: 1.42, 95% CI: 1.18-1.71; Systematic Review Registration: www.crd.york.ac.uk/PROSPERO/, identifier CRD42021252500.

Indexed as

chemotherapygynecological cancermeta-analysisnetwork pharmacologytraditional Chinese medicine

Identifiers

PMID35875080
PMCPMC9298573
OpenAlexW4283830497

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.