Interrater reliability of Chinese medicine diagnosis in people with prediabetes

Suzanne J. Grant, Rosa N. Schnyer, Dennis Hsu-Tung Chang, Paul Fahey, Alan Bensoussan

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)

    Abstract

    Background. Achieving reproducibility in research design is challenging when patient cohorts under study are inconsistently defined. Traditional Chinese medicine (TCM) diagnosis is one example where inconsistency between practitioners has been found. We hypothesise that the use of a validated instrument may improve consistency. Biochemical biomarkers may also be used enhance reliability. Methods. Twenty-seven participants with prediabetes were assessed by two TCM practitioners using a validated instrument (TEAMSI-TCM). Inter-rater reliability was summarised using percentage agreement and the kappa coefficient. One-way ANOVA and Tukey’s post hoc test were used to test links between TCM diagnosis and biomarkers. Results. The two practitioners agreed on primary diagnosis of 70% of participants. kappa = 0.56 (P<0.001). The three predominant TCM diagnostic patterns for people with prediabetes were Yin deficiency, Qi and Yin deficiency and Spleen qi deficiency. The Spleen Qi deficiency with Damp cohort had statistically significant higher fasting glucose, higher insulin, higher insulin resistance, higher HbA1c and lower HDL than those with Qi and Yin deficiency. Conclusions. Using the TEAMSI-TCM resulted in moderate interrater reliability between TCM practitioners. This study provides initial evidence of variation in the biomarkers of people with prediabetes according to the different TCM patterns which may suggest a route to further improving interrater reliability.
    Original languageEnglish
    Article number710892
    Pages (from-to)1-8
    Number of pages8
    JournalEvidence-Based Complementary and Alternative Medicine
    Volume2013
    DOIs
    Publication statusPublished - 2013

    Keywords

    • diagnosis
    • medicine, Chinese
    • prediabetic state

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