A Brief Digital Cognitive Assessment for Detection of Cognitive Impairment in Cuban Older Adults
Bibliographic record
Abstract
BACKGROUND: Rapid technological advances offer a possibility to develop cost-effective digital cognitive assessment tools. However, it is unclear whether these measures are suitable for application in populations from Low and middle-income countries (LMIC). OBJECTIVE: To examine the accuracy and validity of the Brain Health Assessment (BHA) in detecting cognitive impairment in a Cuban population. METHODS: In this cross-sectional study, 146 participants (cognitively healthy = 53, mild cognitive impairment (MCI) = 46, dementia = 47) were recruited at primary care and tertiary clinics. The main outcomes included: accuracy of the BHA and the Montreal Cognitive Assessment (MoCA) in discriminating between controls and cognitively impaired groups (MCI and dementia) and correlations between the BHA subtests of memory, executive functions, and visuospatial skills and criterion-standard paper-and-pencil tests in the same domains. RESULTS: The BHA had an AUC of 0.95 (95% CI: 0.91-0.98) in discriminating between controls and cognitively impaired groups (MCI and dementia, combined) with 0.91 sensitivity at 0.85 specificity. In discriminating between control and MCI groups only, the BHA tests had an AUC of 0.94 (95% CI: 0.90-0.99) with 0.71 sensitivity at 0.85 specificity. Performance was superior to the MoCA across all diagnostic groups. Concurrent and discriminant validity analyses showed moderate to strong correlations between the BHA tests and standard paper-and-pencil measures in the same domain and weak correlations with standard measures in unrelated domains. CONCLUSION: The BHA has excellent performance characteristics in detecting cognitive impairment including dementia and MCI in a Hispanic population in Cuba and outperformed the MoCA. These results support potential application of digital cognitive assessment for older adults in LMIC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".