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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".