Literature DB >> 1872232

Self-referred mammography patients: analysis of patients' characteristics.

H E Reynolds1, V P Jackson.   

Abstract

Among mammography patients, a small but growing group of highly motivated women refer themselves directly for screening without the suggestion of their physicians. We surveyed 485 patients during a 3-month period to study how self-referred mammography patients differ from physician-referred patients. Self-referred patients were more likely than physician-referred patients to have a family income of more than $30,000 per year, to be college graduates, and to consider their health as good or excellent. A large percentage of self-referred patients performed other health-promoting practices, but were not significantly more likely to do these than were physician-referred patients. Women who referred themselves were more likely to have a friend with breast cancer and to believe that cancer could be cured. They expressed much less worry about radiation exposure and were more likely to consider $50.00 an appropriate charge for a screening mammogram. By far, the greatest motivator for self-referred patients was health promotion and disease prevention. Self-referred mammography patients tend to be wealthier, more education, and less concerned about the cost and radiation dose of mammography when compared with physician-referred mammography patients.

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Year:  1991        PMID: 1872232     DOI: 10.2214/ajr.157.3.1872232

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  3 in total

1.  Factors associated with perceived risk of breast cancer among women attending a screening program.

Authors:  S W Vernon; V G Vogel; S Halabi; M L Bondy
Journal:  Breast Cancer Res Treat       Date:  1993-11       Impact factor: 4.872

2.  Early detection of breast cancer using a self-referral mammography process: the Kaiser Permanente Northwest 20-year history.

Authors:  David Moiel; John Thompson
Journal:  Perm J       Date:  2014

3.  Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization.

Authors:  Akiyoshi Hizukuri; Ryohei Nakayama; Mayumi Nara; Megumi Suzuki; Kiyoshi Namba
Journal:  J Digit Imaging       Date:  2020-11-06       Impact factor: 4.056

  3 in total

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