Evidence map›Paper›PMID 33778488›Full record

ReviewJournal of breast imaging

Assessing Risk of Breast Cancer: A Review of Risk Prediction Models.

Geunwon Kim, Manisha Bahl

Abstract readReview
In one paragraph

Review in Journal of breast imaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 2 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

49 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  16. Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025
    Review
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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

2 authors.

Geunwon KimBeth Israel Deaconess Medical Center, Department of Radiology, Boston, MA, USA.
Manisha BahlMassachusetts General Hospital, Department of Radiology, Boston, MA, USA.

Funding

Development and Clinical Implementation of an Artificial Intelligence Tool to Predict Risk of Upgrade of Ductal Carcinoma In SituK08CA241365 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BAHL, MANISHA · 2019 to 2023
$1.3M
NCI NIH HHS K08 CA241365
6 · The paper itself

Abstract

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a

Indexed as

breast cancermammographyrisk assessmentscreening

Identifiers

PMID33778488
PMCPMC7980704

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.