Evidence map›Paper›PMID 23797567›Full record

SynthesisClinical and experimental medicine2014

The diagnostic value of monoclonal gastric cancer 7 antigen: a systematic review with meta-analysis.

Zongyue Zeng, Sheng Fu, Ping Hu, Liuyang Zhao, Hailong Zhang, Xi Tang, Xiaorong Yang, Zhaofang Zeng

Abstract readEvaluation StudyMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Clinical and experimental medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. [Advances in serum biomarkers for early diagnosis of gastric cancer].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2019
    Review
  4. Article
  5. 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

8 authors.

Zongyue Zeng
Sheng Fu
Ping Hu
Liuyang Zhao
Hailong Zhang
Xi Tang
Xiaorong Yang
Zhaofang Zeng

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to explore the clinical characteristic and the diagnostic role of MG7-Ag in detecting gastric cancer (GC) through a systematic review and meta-analysis. Relevant manuscripts aiming at the application of serum MG7-Ag level in GC diagnosis were searched in PubMed, Embase, Chinese National Knowledge Infrastructure, VIP, and Wan Fang Data independently,which were published between January 1, 1980 and February 28, 2013. The pooled sensitivity, specificity,positive diagnostic likelihood ratio (DLR+), negative diagnostic likelihood ratio (DLR-), diagnostic odds ratio,and the area under the summary receiver operating characteristic(AUC) were used to evaluate the value of serum MG7-Ag in diagnosis of GC by using the Meta-DiSc and STATA 11.0 statistical software. 410 manuscripts were retrieved, and 7 manuscripts of high quality including 652 patients were of high quality in this meta-analysis. Overall,the pooled sensitivity, specificity, DLR+, DLR-, and AUC were 0.73 (95 % CI 0.63-0.82), 0.91 (95 % CI 0.84-0.94), 8.59 (95 % CI 5.62-13.11), 0.29 (95 % CI 0.21-0.42), and 0.92 (95 % CI 0.89-0.94), respectively. MG7-Ag is a potential biomarker for the diagnosis of GC.However, more studies are needed to confirm the standard criteria.

Indexed as

Antigens, NeoplasmBiomarkers, TumorHumansPredictive Value of TestsROC CurveSensitivity and SpecificitySerumStomach NeoplasmsAntigens, NeoplasmBiomarkers, TumorMG7 antigen, human

Identifiers

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.