Cross validation of the Montreal Cognitive Assessment in community dwelling older adults residing in the Southeastern US
Bibliographic record
Abstract
OBJECTIVE: Cross validation study of the MoCA for the detection of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) in a community-based cohort residing in the Southeastern United States. METHODS: One hundred and eighteen English-speaking older adults, who underwent diagnostic evaluation as part of an on-going prospective study, were administered the MoCA and MMSE. Twenty were diagnosed with AD, 24 met criteria for amnestic MCI and 74 were considered cognitively normal. Sensitivities and specificities were calculated using the recommended cut-off scores and ROC curve analyses were performed to determine optimal sensitivity and specificity. The influence of age, education and gender on MoCA score was also examined. RESULTS: Using a cut-off score of 24 or below, the MMSE was insensitive to cognitive impairment. Using the recommended cut-off score of 26, the MoCA detected 97% of those with cognitive impairment but specificity was fair (35%). Using a lower cut-off score of 23, the MoCA exhibited excellent sensitivity (96%) and specificity (95%). CONCLUSION: The MoCA appears to have utility as a cognitive screen for early detection of AD and for MCI and warrants further investigation regarding its applicability in primary care settings, varying ethnic groups, and younger at-risk individuals.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".