A Community-Based Study of Dementia in Mexican American and Non-Hispanic White Individuals
Bibliographic record
Abstract
BACKGROUND: Little information is available on the prevalence of cognitive impairment in Mexican American persons. OBJECTIVE: To determine the prevalence of mild cognitive impairment (MCI) and dementia in those 65 years and older among Mexican American and non-Hispanic white individuals in a community. METHODS: This was a population-based cohort study in Nueces County, Texas, USA. Participants were recruited using a random housing sample. The Harmonized Cognitive Assessment (HCAP) participant and informant protocol was performed after Montreal Cognitive Assessment (MoCA) screening. An algorithm was used to sort participants into diagnostic categories: no cognitive impairment, MCI, or dementia. Logistic regression determined the association of ethnicity with MCI and dementia controlling for age, gender, and education. RESULTS: 1,901 participants completed the MoCA and 547 the HCAP. Mexican Americans were younger and had less educational attainment than non-Hispanic whites. Overall, dementia prevalence was 11.6% (95% CI 9.2-14.0) and MCI prevalence was 21.2% (95% CI 17.5-24.8). After adjusting for age, gender, and education level, there was no significant ethnic difference in the odds of dementia or MCI. Those with ≤11 compared with ≥16 years of education had much higher dementia [OR = 4.9 (95% CI 2.2-11.1)] and MCI risk [OR = 3.5 (95% CI 1.6-7.5)]. CONCLUSIONS: Dementia and MCI prevalence were high in both Mexican American and non-Hispanic white populations. Mexican American persons had double the odds of mild cognitive impairment and this was attenuated when age and educational attainment were considered. Educational attainment was a potent predictor of cognitive impairment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".