The DOI matches its Crossref record, with a small difference in title or year.
New Federal Requirements to Inform Patients About Breast Density: Will They Help Patients?
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Brentnall et al incorporated mammographic breast density into the Gail risk model and Tyrer-Cuzick model and found that their areas under the receiver operating characteristic curve (AUCs) improved from 0.55 and 0.57 to 0.59 and 0.61, respectively.
“For instance, Brentnall et al101 incorporated mammographic breast density into the Gail risk model and Tyrer-Cuzick model and found that their areas under the receiver operating characteristic curve (AUCs) improved from 0.55 and 0.57 to 0.59 and 0.61, respectively.”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [101]
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Research works printed but not linked: 7
- McCarthy, 2018, Breast cancer with a poor prognosis diagnosed after screening mammography with negative results., JAMA Oncol, 4, 998, 10.1001/jamaoncol.2018.0352 · DOI
- Gierach, 2012, Relationship between mammographic density and breast cancer death in the Breast Cancer Surveillance Consortium., J Natl Cancer Inst, 104, 1218, 10.1093/jnci/djs327 · DOI
- Sprague, 2014, Prevalence of mammographically dense breasts in the United States., J Natl Cancer Inst, 106, dju255, 10.1093/jnci/dju255 · DOI
- Melnikow, 2016, Supplemental screening for breast cancer in women with dense breasts: a systematic review for the US Preventive Services Task Force., Ann Intern Med, 164, 268, 10.7326/M15-1789 · DOI
- Lee, 2019, Performance of screening ultrasonography as an adjunct to screening mammography in women across the spectrum of breast cancer risk, JAMA Intern Med, 10.1001/jamainternmed.2018.8372 · DOI
- Rafferty, 2016, Breast cancer screening using tomosynthesis and digital mammography in dense and nondense breasts., JAMA, 315, 1784, 10.1001/jama.2016.1708 · DOI
- Kressin, 2016, Content, readability, and understandability of dense breast notifications by state., JAMA, 315, 1786, 10.1001/jama.2016.1712 · DOI
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Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review
Kenner, B., Chari, S. T., Kelsen, D., Klimstra, D. S., Pandol, S. J., Rosenthal, M., Rustgi, A. K., Taylor, J. A., Yala, A., Abul-Husn, N., et al. (2021) · Pancreas · doi:10.1097/mpa.0000000000001762 · Cited by 158
Metadata
- Authors
- Keating, N. L., & Pace, L. E.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2019-06-18
- Volume
- 321
- Issue
- 23
- Pages
- 2275
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2944435511
- Cited by
- 28
- References
- 0
- Citations by year
- 2025: 2 · 2024: 5 · 2022: 1 · 2021: 7 · 2020: 12 · 2019: 1
- FWCI
- 3.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Keywords
- breast density, mammography, patient information, federal regulation, informed consent, breast cancer screening
- MeSH terms
- Breast Density, Biopsy, Breast, Breast Neoplasms, False Positive Reactions, Female, Humans, Magnetic Resonance Imaging, Mammography, Risk Factors, United States, United States Food and Drug Administration, Ultrasonography, Mammary, Disclosure, Comprehension, Government Regulation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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