Evidence map›Paper›PMID 31348264›Full record

ArticleMedicine2019

Biomarkers for detecting prostate cancer: Protocol for an umbrella review with integrated network meta-analysis.

Junhai Jia, Yue Sun, Jingjie Ren, Muyang Li, Jiancheng Wang, Haiyang Li, Cancer Biomarker Assessment Working Group

Abstract read
In one paragraph

Article in Medicine, 2019. 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
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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

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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

7 authors.

Junhai JiaGansu Province Hospital Rehabilitation Center.
Yue SunSchool of Nursing, Lanzhou University.
Jingjie RenSchool of Nursing, Lanzhou University.
Muyang LiThe Second Clinical Medical College of Lanzhou University.
Jiancheng WangHospital Management Research Center, Lanzhou University.
Haiyang LiGansu Provincial Hospital, Lanzhou, Gansu Province, China.
Cancer Biomarker Assessment Working Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProstate cancer (PCa) is common, with it being the 2nd most prevalent cancer in men worldwide and the 6th leading cause of death in men. Screening for any type of cancer aims to increase the chances of successful treatment through early detection of the disease. There were some systematic reviews (SRs) evaluated the diagnostic value of biomarkers for the diagnosis of PCa and no studies have been conducted to analyze the quality of these SRs. We are not clear which kind of marker is the best choice. Thus, this study aims to assess the methodologic quality of the SRs and reanalyze the published data based on SRs for the biomarkers to find the optimal biomarker for the early diagnosis of PCa.

methodsWe performed a systematic literature search of PubMed, Embase, Web of Science, and Cochrane Library and to identify relevant SRs from inception to April 2019. Diagnostic accuracy studies included any type of single biomarker or combined biomarkers aimed at evaluating the diagnostic value is considered eligible for this overview. The Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument will be used to evaluate the risk of bias of the included SRs. Standard pairwise meta-analysis and adjusted indirect comparison will be used to compare the diagnostic value of different biomarkers.

resultsThe results of this study will be submitted to a peer-reviewed journal for publication.

conclusionThis study will reanalyze the published data based on SRs. We hope that the results will help find a biomarker with the superior diagnostic performance for the diagnosis of PCa. PROSPERO REGISTRATION NUMBER: CRD42019125880.

Indexed as

Biomarkers, TumorEarly Detection of CancerHumansMaleNetwork Meta-Analysis as TopicProstatic NeoplasmsResearch DesignReview Literature as TopicSensitivity and SpecificitySystematic Reviews as TopicBiomarkers, Tumor

Identifiers

PMID31348264
PMCPMC6708917

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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.