Evidence map›Paper›PMID 31681404›Full record

ArticleFrontiers in genetics2019

Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets.

Zhitong Bing, Yuxiang Yao, Jie Xiong, Jinhui Tian, Xiangqian Guo, Xiuxia Li, Jingyun Zhang, Xiue Shi, Yanying Zhang, Kehu Yang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2019. 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
0.1field-weighted citation impact, top 58% 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. 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

10 authors at 5 institutions in 1 country.

Zhitong BingEvidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
Yuxiang YaoSchool of Physical Science and Technology, Lanzhou University, Lanzhou, China.
Jie XiongDepartment of Applied Mathematics, Changsha University, Changsha, China.
Jinhui TianEvidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
Xiangqian GuoMedical Bioinformatics Institute, School of Basic Medicine, Henan University, Henan, China.
Xiuxia LiEvidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
Jingyun ZhangEvidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
Xiue ShiInstitute for Evidence Based Rehabilitation Medicine of Gansu Province, Lanzhou, China.
Yanying ZhangDepartment of Pharmacology and Toxicology of Traditional Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China.
Kehu YangEvidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
Lanzhou University · CNChangsha University · CNChinese Academy of Sciences · CNGansu University of Traditional Chinese Medicine · CNHenan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined

Indexed as

Cox regressiongene signatureovarian cancerprognosis indexrobust prognostic model

Identifiers

PMID31681404
PMCPMC6798149
OpenAlexW2971779013

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.