Cognitive Function and Incidence of Stroke in Older Mexican Americans
Bibliographic record
Abstract
BACKGROUND: Given the high prevalence of cognitive impairment in older Mexican Americans and limited longitudinal research examining cognitive function in this ethnic group, we conducted a study examining whether cognitive impairment is a risk factor for new onset of stroke among older Mexican Americans. METHODS: We performed a prospective cohort study of 2682 Mexican Americans aged 65 years and older living in the southwestern United States. For subjects with no prior history of stroke and who completed the Mini-Mental State Examination (MMSE) at baseline, stroke incidence was assessed after 2, 5, and 7 years of follow-up. RESULTS: In Cox proportional regression models, MMSE score at baseline predicted risk of incident stroke over a 7-year follow-up period. For the unadjusted model, subjects with an MMSE score of 21 or higher were half as likely to report stroke at follow-up (hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.35-0.69; p <.001) compared with those with a score of less than 21. We found similar results after controlling for relevant risk factors for stroke including age, gender, smoking status, education, body mass index, diabetes, heart attack, systolic blood pressure, and depressive symptoms (HR, 0.54; 95% CI, 0.38-0.77; p =.001). Additionally, each 1-point increase in MMSE score was associated with a 5% reduction in risk (HR, 0.95; 95% CI, 0.92-0.99; p =.01). CONCLUSIONS: Increasing MMSE score is associated with a decreasing incidence of stroke in older Mexican Americans. This study highlights the need for a more aggressive focus on identifying and addressing cognitive decline in the Mexican American population.
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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 | 0.002 |
| 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 | 0.000 | 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".