Detection of Mild Cognitive Impairment Among Community-Dwelling African Americans Using the Montreal Cognitive Assessment
Bibliographic record
Abstract
OBJECTIVE: To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample. METHODS: A total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing. Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line. ROC results were compared with previously published MoCA cut scores. RESULTS: The MCI group was slightly older (MMCI = 64.76[5.87], MNC = 62.33[6.76]; p = .033) and less educated (MMCI = 13.07[2.37], MNC = 14.36[2.51]; p = .004) and had lower MoCA scores (MMCI=21.26[3.85], MNC = 25.47[2.13]; p < .001) than the NC group. Demographics were non-significant in regression models. The area under the curve (AUC) was significant (MoCA = .83, p < .01) and an optimal cut score of <24 maximized sensitivity (72%), specificity (84%), and provided 76% diagnostic accuracy. In comparison, the traditional cut score of <26 had higher sensitivity (84%), similar accuracy (76%), but much lower specificity (58%). CONCLUSIONS: This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study underscores the importance of culturally appropriate norms to optimize the utility of commonly used cognitive screening measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".