Evidence map›Paper›PMID 34168953›Full record

ArticlePreventive medicine reports2021

A framework for personalized mammogram screening.

Dinesh Pal Mudaranthakam, Michele Park, Jeffrey Thompson, Alexander M Alsup, Ron Krebill, Lynn Chollet Hinton, Jinxiang Hu, Byron Gajewski, Andrew Godwin, Matthew S Mayo and 4 more

Open access · goldAbstract read
In one paragraph

Article in Preventive medicine reports, 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
0.2field-weighted citation impact, top 47% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

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

14 authors at 2 institutions in 1 country.

Dinesh Pal MudaranthakamDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Michele ParkThe University of Kansas Cancer Center, Kansas City, KS, USA.
Jeffrey ThompsonDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Alexander M AlsupDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Ron KrebillDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Lynn Chollet HintonDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Jinxiang HuDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Byron GajewskiDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Andrew GodwinThe University of Kansas Cancer Center, Kansas City, KS, USA.
Matthew S MayoDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Jo WickDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Lisa Harlan-WilliamsThe University of Kansas Cancer Center, Kansas City, KS, USA.
Jianghua HeDepartment of Biostatistics & Data Science, University of Kansas, Medical Center, Kansas City, KS, USA.
Tami Gurley-CalvezPopulation Health, University of Kansas, Medical Center, Kansas City, KS, USA.
The University of Kansas Cancer Center · USUniversity of Kansas Medical Center · US

Funding

Using Integrated Omics to Identify Dysfunctional Genetic Mechanisms Influencing Schizophrenia and Sleep DisturbancesP20GM130423 · NIGMS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Kalyani Pyaram, Steven Allan Soper · 2019 to 2026
$21.5M
NIGMS NIH HHS P20 GM130423
6 · The paper itself

Abstract

Breast cancer screening guidelines serve as crucial evidence-based recommendations in deciding when to begin regular screenings. However, due to developments in breast cancer research and differences in research interpretation, screening guidelines can vary between organizations and within organizations over time. This leads to significant lapses in adopting updated guidelines, variable decision making between physicians, and unnecessary screening for low to moderate risk patients (Jacobson and Kadiyala, 2017; Corbelli et al., 2014). For analysis, risk factors were assessed for patient screening behaviors and results. The outcome variable for the first analysis was whether the patient had undergone screening. The risk factors considered were age, marital status, education level, rural versus urban residence, and family history of breast cancer. The outcome variable for the second analysis was whether patients who had undergone breast cancer screening presented abnormal results. The risk factors considered were age, Body Mass Index, family history, smoking and alcohol status, hormonal contraceptive use, Hormone Replacement Therapy use, age of first pregnancy, number of pregnancies (parity), age of first menses, rural versus urban residence, and whether or not patients had at least one child. Logistic regression analysis displayed strong associations for both outcome variables. Risk of screening nonattendance was negatively associated with age as a continuous variable, age as a dichotomous variable, being married, any college education, and family history. Risk of one or more abnormal mammogram findings was positively associated with family history, and hormonal contraceptive use. This procedure will be further developed to incorporate additional risk factors and refine the analysis of currently implemented risk factors.

Indexed as

Breast cancer screeningCancer risk factorsMammogramPreventive task force

Identifiers

PMID34168953
PMCPMC8209666
OpenAlexW3171756900

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.