Is the Montreal cognitive assessment culturally valid in a diverse geriatric primary care setting? Lessons from the Bronx
Bibliographic record
Abstract
BACKGROUND: Efficacy and validity of the MoCA for cognitive screening in ethnoculturally and linguistically diverse settings is unclear. We sought to examine the utility and discriminative validity of the Spanish and English MoCA versions to identify cognitive impairment among diverse community-dwelling older adults. METHODS: Participants aged ≥65 with cognitive concerns attending outpatient primary care in Bronx, NY, were recruited. MoCA and neuropsychological measures were administered in Spanish or English, and a neuropsychologist determined cognitive status (normal with subjective cognitive concerns [SCC], mild cognitive impairment [MCI], and dementia). One-way ANOVA compared cognitive statuses. ROC analyses identified optimal MoCA cutpoints for discriminating possible cognitive impairment. RESULTS: There were 231 participants, with mean age 73, 72% women, 43% Hispanic; 39% Black/African American; 113 (49%) completed testing in English and 118 (51%) in Spanish. Overall MoCA mean was 17.7 (SD = 4.3). Neuropsychological assessment identified 90 as cognitively normal/SCC, average MoCA 19.9 (SD = 4.1), 133 with MCI, average MoCA 16.6 (SD = 3.7), and 8 with dementia, average MoCA 10.6 (SD = 3.1). Mean English MoCA average was 18.6 (SD = 4.1) versus Spanish 16.7 (SD = 4.3). The published cutpoint ≤23 for MCI yielded a high false-positive rate (79%). ROC analyses identified ≤18.5 as the score to identify MCI or dementia using the English MoCA (65% sensitivity; 77% specificity) and ≤16.5 for the Spanish MoCA (64% sensitivity;73% specificity) in this sample of older adults with cognitive concerns. CONCLUSIONS: Current MoCA cutpoints were inappropriately high in a culturally/linguistically diverse urban setting, leading to a high false-positive rate. Lower Spanish and English MoCA cutpoints may improve diagnostic accuracy for identifying cognitive impairment in this group, highlighting the need for the creation and validation of accurate cognitive screeners for ethnoculturally and linguistically diverse older adults.
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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 | 0.040 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".