Prevalence of Mild Cognitive Impairment and Its Subtypes in the Mexican Population
Bibliographic record
Abstract
BACKGROUND/AIM: To estimate the prevalence of mild cognitive impairment (MCI) and its subtypes, taking into account education and health status. METHODS: This is the first report of our Study on Aging and Dementia in Mexico. This study included 2,944 elderly individuals 60 years old or more with in-home assessment for cognitive impairment. The prevalence of MCI was based on Petersen criteria. MCI was classified as amnestic of single domain (a-MCI-s) or multiple domain (a-MCI-md) or nonamnestic of single domain (na-MCI-s) or multiple domain (na-MCI-md). In addition to a battery of neuropsychological measures, a self-report depression measure and a medical history including history of stroke, heart disease and other health conditions were recorded. RESULTS: The global estimated prevalence of MCI in the Mexican population was 6.45%. Of these subjects, 2.41% met criteria for a-MCI-s, 2.56% for a-MCI-md, 1.18% for na-MCI-s and 0.30% for na-MCl-md. Women showed a higher prevalence of MCI than men (63.7 vs. 36.3%, respectively). The analysis showed that heart disease [odds ratio (OR) 1.5], stroke (OR 1.2) and depression (OR 2.1) were associated with an increased risk of MCI. CONCLUSIONS: The prevalence of MCI in Mexico is similar to that in other countries. The results suggest that stroke, heart disease and depression may have an important role in the etiology of MCI.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".