MONTREAL COGNITIVE ASSESSMENT SURVEY ADAPTATION IN A COMMUNITY-BASED OLDER ADULT SAMPLE, 2019–2020 NHANES
Bibliographic record
Abstract
Abstract The U.S. population is aging. Some degree of cognitive change is expected as a natural part of healthy aging. Understanding the prevalence of potential impairments in community-based samples is necessary for prioritization of public health efforts. We analyzed Montreal Cognitive Assessment Survey Adaptation (MoCA-SA) data from the 2019-2020 National Health and Nutrition Examination Survey (NHANES). The full MoCA-SA was administered to a convenience sample of 1,123 individuals aged ≥60 years. Excluding incomplete inventories yielded a final analytic sample of 895. The MoCA-SA contains 20 items and is scored 0-20, with values < 17 indicative of lower cognitive performance. Scores were also analyzed for 8 subscales: orientation, naming, visual construction, executive function, attention, language fluency, abstraction, and memory. Estimates were stratified across three age categories: 60-69, 70-79, and ≥80 years. Overall, 71.2% (unweighted; 95% CI 68.2-74.1) of participants aged ≥60 years scored < 17. People aged ≥80 years scored significantly lower on the orientation and memory and higher on abstraction than those aged 60-69 years. No age-related differences were identified in the remaining subscales. While changes in cognition are a normal part of aging, not all changes are healthy. Only a healthcare provider can diagnose cognitive impairments such as Alzheimer’s disease. People experiencing changes in cognition that interfere with daily life should speak with their healthcare provider about their concerns and ways to promote brain health. Additional work is needed to determine how the MoCA-SA corresponds to cognitive functioning in daily life and which subscales may drive differences in community samples.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".