Evidence map›Paper›PMID 32923852›Full record

ArticleJCO precision oncology2019

Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes.

Jerome Jourquin, Stephanie Birkey Reffey, Cheryl Jernigan, Mia Levy, Glendon Zinser, Kimberly Sabelko, Jennifer Pietenpol, George Sledge

Abstract read
In one paragraph

Article in JCO precision oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Real-world evidence enhances decision making.Journal of managed care & specialty pharmacy · 2020
    Article
  7. Review
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

8 authors.

Jerome JourquinSusan G. Komen, Dallas, TX.
Stephanie Birkey ReffeySusan G. Komen, Dallas, TX.
Cheryl JerniganUniversity of Kansas Cancer Center, Kansas City, KS.
Mia LevyRush University Medical Center, Chicago IL.
Glendon ZinserSusan G. Komen, Dallas, TX.
Kimberly SabelkoSusan G. Komen, Dallas, TX.
Jennifer PietenpolVanderbilt University, Nashville, TN.
George SledgeStanford University School of Medicine, Stanford, CA.

Funding

TISSUE CoreP50CA098131 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PARK, BEN HO, PIETENPOL, JENNIFER A · 2003 to 2024
$48.7M
NCI NIH HHS P50 CA098131
6 · The paper itself

Abstract

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

Identifiers

PMID32923852
PMCPMC7446366

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.