![]() ![]() For MMSE, it turned out that more important cut-off was of 27/28 (n = 882, 66.34% sensitivity and specificity of 72.94%). Results: ROC curve analysis for MoCA demonstrated that MCI best detection can be achieved with a cut-off point of 24/25 (n = 9350, the sensitivity of 80.48% and specificity of 81.19%). The cut-offs are shown as ROC curve and accuracy of diagnosis for MoCA and MMSE was calculated as the area under the curve (AUC). Research credibility was established by computing weighted arithmetic mean, where weight is defined as population for which the result of sensitivity and specificity for the cut-off point was achieved. At the end, for the evaluation of MoCA 20, and MMSE 13 studies were qualified. Papers which met inclusion and exclusion criteria were chosen to be included in this review. ![]() The following medical subject headings were used in the search: mild cognitive impairment, mini-mental state examination, Montreal cognitive assessment, diagnostics value. Methods: A systematic literature search was carried out by the authors using EBSCO host Web, Wiley Online Library, Springer Link, Science Direct and Medline databases. MMSE credibility assessment in detecting MCI, while taking into consideration the sensitivity and specificity by cut-off points. The Montreal Cognitive Assessment (MoCA), was created as an alternative method for MMSE.Īim. Nowadays, the MiniMental State Examination (MMSE) is the most commonly used scale in cognitive function evaluation, albeit it is claimed to be imprecise for MCI detection. Conclusion: The MoCA's ability to discriminate MCI from NC was modest in this Chinese population, because it was far more sensitive to the effect of education than MCI diagnosis.Objectives: Screening tests play a crucial role in dementia diagnostics, thus they should be very sensitive for mild cognitive impairment (MCI) assessment. In multivariate analyses controlling for age and gender, MCI diagnosis was associated with a <1-point decrement in MoCA score (η 2 = 0.010), but lower (1-6 years) and no education was associated with a 3- to 5-point decrement (η 2 = 0.115 and η 2 = 0.162, respectively). Overall, the MoCA's test performance was not better than that of the MMSE. The MoCA's test performance was least satisfactory in the highest (>6 years) education group: AUC = 0.50 (p = 0.98), Sn = 0.54, and Sp = 0.51 at a cut-off of 27/28. ![]() Results: The MoCA modestly discriminated MCI from NC in both study samples (AUC = 0.63 and 0.65): Sn = 0.64 and Sp = 0.36 at a cut-off of 28/29 in the clinic-based sample, and Sn = 0.65 and Sp = 0.55 at a cut-off of 22/23 in the community-based sample. Method: The MoCA and Mini-Mental State Examination (MMSE) were evaluated in two independent studies (clinic-based sample and community-based sample) of MCI and normal cognition (NC) controls, using receiver operating characteristic curve analyses: area under the curve (AUC), sensitivity (Sn), and specificity (Sp). ![]() We evaluated the MoCA's test performance by educational groups among older Singaporean Chinese adults. Background: The Montreal Cognitive Assessment (MoCA) was developed as a screening instrument for mild cognitive impairment (MCI). ![]()
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