Optimal cut‐off scores for mild cognitive impairment and dementia of the Spanish version of the Montreal Cognitive Assessment in Costa Rican older adults
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease and related dementias (AD/ADRD) burden in Costa Rica is expected to become one of the highest in the region. Early detection will help optimize resources and improve primary care interventions. The Montreal Cognitive Assessment (MoCA) has shown good sensitivity for detection of mild cognitive impairment (MCI), but specificity varies depending on the population. This motivates the analysis of different cut-offs to minimize false-positive classifications in a Costa Rican sample for its use in clinical settings. METHODS: Data was analyzed from 516 memory clinic outpatients (148 cognitively normal, 260 MCI, 108 mild dementia; mean age 66.3 ± 10.8 years) who underwent complete neurological and neuropsychological assessment and were diagnosed by consensus. Optimal MoCA cut-off scores were identified using the Youden's Index, balanced cut-offs, and specificity scores. RESULTS: Overall, a cut-off score of ≥23 showed better accuracy to distinguish between NC and MCI (sensitivity 73%, specificity 83%). When analyzed by educational levels, cut-off scores of ≥21 showed better accuracy for ≤6 years (sensitivity 80%, specificity 76%), ≥23 for 7-12 years (sensitivity 86%, specificity 76%) and ≥24 for ≥12 years (sensitivity 70%, specificity 85%). For distinguishing MCI from mild dementia, the optimal overall cut-off score was ≥15 (sensitivity 65%, specificity 85%). When stratified by years of education, cut-off scores of ≥14 showed better accuracy for ≤6 years (sensitivity 70%, specificity 88%), ≥15 for 7-12 years (sensitivity 79%, specificity 95%) and ≥17 for ≥12 years (sensitivity 67%, specificity 93%). CONCLUSIONS: A MoCA cut-off score of ≥23 in Costa Rican population showed better diagnostic accuracy and may reduce the false positive rate. Our findings may be helpful for primary care clinical settings and further referral criteria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".