A-21Detection of MCI in African Americans Using the Montreal Cognitive Assessment (MoCA)
Bibliographic record
Abstract
Objective: The Montreal Cognitive Assessment (MoCA) is a widely used cognitive screening tool. Our earlier work suggests that published cut-off scores for mild cognitive impairment (MCI) may lead to over-identification of impairment in minorities. This study aimed to develop an appropriate cut-off score to detect MCI in a community-based African American sample. Method: Consensus diagnoses of normal cognition (n = 45) or MCI (n = 90) were given to participants from a community-based cohort. Diagnoses were based on Clinical Dementia Rating score and comprehensive neuropsychological assessments; clinicians were blinded to MoCA results. Receiver operating characteristic (ROC) curve analysis was performed to determine a cut-off score to distinguish MCI from individuals with normal cognition based on optimal sensitivity and specificity, and highest diagnostic accuracy. Results: The cognitively normal group was slightly younger [M.NC = 62.33(6.76), M.MCI = 64.76(5.87); p = 0.033], more educated [M.NC = 14.36(2.51), M.MCI = 13.07(2.37); p = 0.004], and had higher MoCA scores [M.NC = 25.47(2.13), M.MCI = 21.26(3.85); p < 0.001] than the MCI group. A cut-off of 23.5 yielded optimal sensitivity (72.2%) and specificity (84.4%), with 76% accuracy in distinguishing MCI from normals (AUC = 0.826; 95%CI: 0.755–0.896; p < 0.0001). The traditional cut-off of <26 increased sensitivity (84.4%) but lowered specificity (57.8%), and demonstrated similar accuracy (75.6%). Conclusion: This study provides a cut-off score to help differentiate persons with MCI from those with normal cognition in an African American sample. Using a cut-off score of <24 reduces the likelihood of misclassifying cognitively normal individuals as impaired than previously published cut-offs. This study underscores the importance of developing community- and racially-based norms to optimize clinical utility of commonly used 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 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".