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Evidence-Based Complementary and Alternative Medicine
Volume 2015, Article ID 469675, 11 pages
Research Article

Effects of Diagnostic Errors in Pattern Differentiation and Acupuncture Prescription: A Single-Blinded, Interrater Agreement Study

Laboratório de Simulação Computacional e Modelagem em Reabilitação, Programa de Pós-graduação em Ciências da Reabilitação, Centro Universitário Augusto Motta (UNISUAM), 21041-010 Rio de Janeiro, RJ, Brazil

Received 11 January 2015; Revised 14 March 2015; Accepted 15 March 2015

Academic Editor: Hongcai Shang

Copyright © 2015 Ingrid Jardim de Azeredo Souza Oliveira and Arthur de Sá Ferreira. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study compared the interrater agreement for pattern differentiation and acupoints prescription between two groups of human patients simulated with different diagnostic outcomes. Patients were simulated using a dataset about zangfu patterns and separated into groups ( each) according to the diagnostic outcome determined by a computational model. A questionnaire with 90 patients was delivered to 6 TCM experts (4-year minimal of clinic experience) who were asked to indicate a single pattern (among 73) and 8 acupoints (among 378). Interrater agreement was higher for pattern differentiation than for acupuncture prescription. Interrater agreement on pattern differentiation was slight for both groups with correct (Light’s , 95% CI = [0.108; 0.254]) and incorrect diagnosis (Light’s , 95% CI = [0.120; 0.286]). Interrater agreement on acupuncture prescription was slight for both groups of correct (, 95% CI = [0.015; 0.057]) and incorrect diagnosis (, 95% CI = [0.023; 0.058], ). Diagnostic performance of raters yielded the following: accuracy = 60.9%, sensitivity = 21.7%, and specificity = 100%. An overall improvement in the interrater agreement and diagnostic accuracy was observed when the data were analyzed using the internal systems instead of the pattern’s labels.