Evidence map›Paper›PMID 32590731›Full record

ArticleMedicine2020

A comprehensive evaluation of single nucleotide polymorphisms associated with osteosarcoma risk: A protocol for systematic review and network meta-analysis.

Zhuo-Miao Ye, Ming-Bo Luo, Chi Zhang, Jing-Hui Zheng, Hong-Jun Gao, You-Ming Tang

Open access · goldAbstract read
In one paragraph

Article in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 44% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Toll-Like Receptor Polymorphisms and the Risk of Cancer: Meta-analysis Study.Methods in molecular biology (Clifton, N.J.) · 2023
    Pooled it
  2. Article
  3. 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

6 authors at 3 institutions in 1 country.

Zhuo-Miao YeRuikang School of Clinical Medicine.
Ming-Bo LuoRuikang School of Clinical Medicine.
Chi ZhangGraduate School, Guangxi University of Chinese Medicine.
Jing-Hui ZhengDepartment of Cardiology.ORCID 0000-0001-5076-6432
Hong-Jun GaoDepartment of Urinary Surgery, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi.
You-Ming TangDepartment of Gastroenterology, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China.
Guangxi University of Chinese Medicine · CNRuikang Affiliated Hospital of Guangxi Medical University · CNGuangxi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSingle nucleotide polymorphisms (SNPs) have been inconsistently associated with osteosarcoma (OS) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with OS.

methodsDatabases were searched to identify association studies of SNPs and OS published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, and Wan fang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability for noteworthy associations. The methodological quality of data was assessed based on the STrengthening the REporting of Genetic Association Studies statement Stata 14.0 will be used for systematic review and meta-analysis.

resultsThis study will provide a high-quality evidence to find the SNP most associated with OS susceptibility and the best genetic model.

conclusionsThis study will explore which SNP is most associated with OS susceptibility. REGISTRATION: INPLASY202040023.

Indexed as

Polymorphism, Single NucleotideBone NeoplasmsGenetic Predisposition to DiseaseHumansNetwork Meta-Analysis as TopicOsteosarcomaResearch DesignRisk AssessmentSystematic Reviews as Topic

Identifiers

PMID32590731
PMCPMC7328971
OpenAlexW4243371240

What Socratic 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 Socratic graph.