Evidence map›Paper›PMID 33811166›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2021

TDLU Involution and Breast Cancer Risk-Reply.

Yujing J Heng, Kevin H Kensler, Gabrielle M Baker, Laura C Collins, Stuart J Schnitt, Rulla M Tamimi

Abstract readLetterComment
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2021. 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
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yujing J HengDepartment of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts. yheng@bidmc.harvard.edu.ORCID 0000-0002-8930-817X
Kevin H KenslerDivision of Population Sciences, Dana Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0001-7515-7270
Gabrielle M BakerDepartment of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Laura C CollinsDepartment of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Stuart J SchnittDepartment of Pathology, Harvard Medical School and Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts.
Rulla M TamimiDepartment of Population Health Sciences, Weill Cornell Medicine, New York, New York.ORCID 0000-0003-2306-8668

Funding

Life Course Cancer Epidemiology Cohort in WomenU01CA176726 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 2018 to 2025
$22.4M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Mammographic density and texture features in relation to breast cancer riskR01CA175080 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI TAMIMI, RULLA M · 2013 to 2016
$1.7M
Computational pathology to predict breast cancer risk in benign breast diseaseR21CA187642 · NCI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI SCHNITT, STUART, TAMIMI, RULLA M · 2015 to 2016
$439k
NCI NIH HHS R01 CA175080NCI NIH HHS R21 CA187642NCI NIH HHS U01 CA176726NCI NIH HHS UM1 CA186107
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Breast NeoplasmsMammary Glands, HumanBreastFemaleHumansRisk

Identifiers

PMID33811166
PMCPMC8030734

What OpenQuestion 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 OpenQuestion graph.