Does the broad categories for the diagnosis of eating disorders (BCD-ED) scheme reduce the frequency of eating disorder not otherwise specified?
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| Title: | Does the broad categories for the diagnosis of eating disorders (BCD-ED) scheme reduce the frequency of eating disorder not otherwise specified? |
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| Authors: | Sysko, Robyn (AUTHOR), Walsh, B. Timothy (AUTHOR) |
| Source: | International Journal of Eating Disorders. Nov2011, Vol. 44 Issue 7, p625-629. 5p. |
| Subjects: | Diagnosis of eating disorders, Bariatric surgery, Analysis of variance, Chi-squared test, Eating disorders, Interviewing, Longitudinal method, Classification of mental disorders, Self-evaluation, T-test (Statistics), Telephones |
| Abstract: | Objective: This study evaluated whether the Broad Categories for the Diagnosis of Eating Disorders (BCD-ED) proposal (Walsh and Sysko, Int J Eat Disord, 42, 754-764, 2009) reduces the number of individuals who receive a DSM-IV eating disorder not otherwise specified (EDNOS) diagnosis. Method: Individuals calling a tertiary care facility completed a brief telephone interview and were classified into a DSM-IV eating disorder category (anorexia nervosa, bulimia nervosa, EDNOS). Subsequently, the proposed DSM-5 criteria for eating disorders and the BCD-ED scheme were also applied. Results: A total of 247 individuals with telephone interview data met criteria for an eating disorder, including 97 (39.3%) with an EDNOS. Of patients with an EDNOS diagnosis, 97.6% were reclassified using the BCD-ED scheme. Discussion: The BCD-ED scheme has the potential to virtually eliminate the use of DSM-IV EDNOS; however, additional data are needed to document its validity and clinical utility. © 2010 by Wiley Periodicals, Inc. (Int J Eat Disord 2010) [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Eating Disorders is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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