The bicaudate index inversely associates with performance in the Montreal Cognitive Assessment (MoCA) in older adults living in rural Ecuador. The Atahualpa project
Bibliographic record
Abstract
OBJECTIVE: Assessment of cognitive impairment in rural areas of developing countries is complicated by illiteracy and cross-cultural factors. A better way to estimate the usefulness of cognitive screening instruments is to evaluate their correlation with imaging biomarkers. The bicaudate index (a marker of central atrophy) correlates with cognitive performance. We assessed the relationship of the bicaudate index with the MoCA to estimate the usefulness of this test to detect individuals with cognitive decline in these regions. METHODS: Atahualpa residents aged ≥60 years identified during door-to-door surveys were evaluated with the MoCA and invited to undergo brain MRI. Using generalized linear models, we estimated whether the bicaudate index correlates with MoCA scores, after adjusting for demographics and relevant clinical and neuroimaging confounders. RESULTS: Out of 385 eligible persons, 290 (75%) were enrolled. Mean bicaudate index was 0.14 ± 0.03, and mean total MoCA score was 19 ± 5 points. Locally weighted scatterplot smoothing showed a nearly linear inverse relationship between the bicaudate index and the total MoCA score. In the fully adjusted generalized linear model, the bicaudate index was inversely associated with the total MoCA score (p < 0.001), which dropped by 5.3% (95% C.I.: 1.7%-8.8%) for every standard deviation of the bicaudate index. In addition, most domain-specific MoCA scores were inversely associated with the bicaudate index. CONCLUSIONS: The inverse relationship between the bicaudate index and the MoCA score provides evidence that the MoCA is reliable to detect structural brain damage and useful to assess cognitive performance in less educated individuals. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".