Consistency of Traditional Chinese Medicine Diagnoses and Herbal Prescriptions for Rheumatoid Arthritis
Practitioners of Traditional Chinese Medicine (TCM) make diagnoses based solely on clinical symptoms. This study will evaluate whether TCM practitioners make diagnoses consistently.
|Study Design:||Observational Model: Defined Population
Time Perspective: Cross-Sectional
|Official Title:||Consistency of Traditional Chinese Medicine Rheumatoid Arthritis Diagnosis and Herbal Prescriptions|
|Study Start Date:||November 2001|
|Estimated Study Completion Date:||April 2004|
TCM is one of the oldest and most widely-used indigenous medical systems in the world. It is difficult to effectively practice or evaluate TCM outside of its core framework, which includes a diagnostic system that is more conceptual and less technologically driven than that of conventional medicine. TCM diagnosis relies entirely on clinical symptoms and signs that are discerned by the practitioner. The establishment of diagnostic and prescriptive consistency is crucial for efficacy studies involving individualized treatments within the TCM framework. This study will examine the consistency of TCM diagnoses and herbal prescriptions under controlled conditions.
Rheumatoid arthritis (RA) patients will be individually evaluated by three TCM practitioners; the diagnostic and prescriptive behaviors of the practitioners will be recorded and analyzed to determine: 1) the extent to which two or more TCM practitioners assign comparable TCM diagnoses to the same RA patients; 2) the extent to which two or more TCM practitioners prescribe comparable herbal formulas to the same RA patients; and 3) the extent to which TCM diagnoses are related to conventional clinical assessments and biomedical tests of RA. Variables that cause inconsistency will be identified and analyzed.
Please refer to this study by its ClinicalTrials.gov identifier: NCT00071149
|United States, Maryland|
|University of Maryland|
|Baltimore, Maryland, United States, 21201|
|Principal Investigator:||Grant G Zhang, PhD||University of Maryland|