Evidence map›Paper›PMID 36902812›Full record

ReviewJournal of clinical medicine2023

Strong Cumulative Evidence of Associations of 6 Single Nucleotide Polymorphisms with Ovarian Cancer Risk: An Umbrella Review.

Ying-Jun Huo, Xiao-Ying Li, Meng Zhang, Chang Gao, Qian Xiao, Yu-Hong Zhao, Song Gao, Ting-Ting Gong, Qi-Jun Wu

Full text readReview
In one paragraph

Review in Journal of clinical medicine, 2023. 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

9 authors.

Ying-Jun HuoDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Xiao-Ying LiDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Meng ZhangDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Chang GaoDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Qian XiaoDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Yu-Hong ZhaoDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Song GaoDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Ting-Ting GongDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.
Qi-Jun WuDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang 110004, China.ORCID 0000-0001-9421-5114

Funding

345 Talent Project of Shengjing Hospital of China Medical University No. M0268345 Talent Project of Shengjing Hospital of China Medical University No. M0701JieBangGuaShuai Project of Liaoning Province No.2021JH1/1040050LiaoNing Revitalization Talents Program No. XLYC1907102Natural Science Foundation of China No. 82073647Natural Science Foundation of China No.82103914Shenyang high level innovative talents support program No. RC190484
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

associationmeta-analysisovarian cancerrisksingle nucleotide polymorphismumbrella review

Identifiers

PMID36902812
PMCPMC10004083

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read11
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 Socratic graph.