Evidence map›Paper›PMID 33740422›Full record

ReviewCancer cell2021

More reliable breast cancer risk assessment for every woman.

Han Liang

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer cell, 2021. 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
0.7field-weighted citation impact, top 27% 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, 4 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

1 author at 1 institution in 1 country.

Han LiangDepartment of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: hliang1@mdanderson.org.
The University of Texas MD Anderson Cancer Center · US

Funding

TRANSLATIONAL AND ANALYTICAL CHEMISTRY COREP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI PETER W PISTERS · 1985 to 2026
$279.3M
TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic DataU24CA209851 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN, MILLS, GORDON B. · 2016 to 2020
$4.0M
Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancerU01CA217842 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DENEEN, BENJAMIN, MILLS, GORDON B. · 2017 to 2021
$3.6M
Characterization and modeling of m6A RNA methylation in cancerR01CA251150 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN · 2020 to 2024
$2.7M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA251150NCI NIH HHS U01 CA217842NCI NIH HHS U24 CA209851
6 · The paper itself

Abstract

Using large, unbiased cohorts, two studies in The New England Journal of Medicine assessed the associations between germline variants in putative cancer susceptibility genes and the risk of breast cancer. They consistently identified a small set of genes as being the most informative for risk prediction, helping select high-risk women in the general population, and developing effective cancer prevention strategies.

Indexed as

Clinical Trials as TopicRisk AssessmentBreast NeoplasmsCase-Control StudiesFemaleGenetic Predisposition to DiseaseGerm-Line MutationHumans

Identifiers

PMID33740422
PMCPMC8244816
OpenAlexW3137306549

What Socratic holds

Textmetadata
LicenceTDM
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.