Socioeconomic and health‐related disparities associated with healthy brain aging in Latin American countries
Bibliographic record
Abstract
Abstract Background Latin American Countries (LACs) have major health‐related inequities due to historical, cultural, and social aspects. These factors have been suggested as important determinants of healthy aging in LACs. Here, we evaluated classic and socioeconomic risk factors for healthy brain aging across five large cohorts of LACs. Method Risk factors for healthy aging were evaluated using machine‐learning models in 41,092 individuals across five LACs ([Brazil, n = 9,412], Colombia [n = 23,694], Chile [n = 1,301], Ecuador [n = 5,235], and Uruguay [n = 1,450] (Fig. 1A). Healthy brain aging was evaluated using z‐scored cognitive and functional ability data with selected risk factors (Age, Sex, Diabetes, Education, Isolation, House Condition, Hypertension, Heart Disease, Alcohol Consumption, Physical Activity, Smoking, Falls, and Mental Health Problems). The fitness of models was evaluated with Mean Standard Error (MSE) and Raw Mean Standard Error (RMSE) extracted from Ridge Regressions Models (adjusted p<0.05). Result Regarding cognition, our machine‐learning model with LACs was significant. The most important risk factors were mental health symptoms, education, country, physical activity, alcohol consumption, falls, socioeconomic status, isolation, age, and smoking status. No significant effects of heart disease, hypertension, sex, and diabetes were found (Fig. 1B). The model assessing functionality in LACs was also significant and presented the following order of risk factors: physical activity, mental health symptoms, falls, heart disease, alcohol consumption, diabetes, sex, hypertension, age, SES, education, education, and smoking status. Country and isolation did not reach statistical significance (Fig. 1C). Conclusion Our findings demonstrated that social and health disparities outweigh classic risk factors, such as age and sex, for cognitive and functional decline in LACs, highlighting the need to identify risk factors for healthy brain aging in underrepresented populations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".