Clinical and neuroimaging risk factors for cognitive decline in community‐dwelling older adults living in rural Ecuador. A population‐based prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: There is limited information on factors influencing cognitive decline in rural settings from low- and middle-income countries. Using the Atahualpa Project cohort, we aimed to assess the burden of cognitive decline in older adults living in a rural Ecuadorian village. METHODS: The study included Atahualpa residents aged greater than or equal to 60 years who had a follow-up Montreal Cognitive Assessment (MoCA) repeated at least 1 year after baseline. MoCA decline was assessed by multivariable longitudinal linear models, adjusted for demographics, days between MoCA tests, cardiovascular risk factors, and neuroimaging signatures of structural brain damage. RESULTS: We included 252 individuals who contributed 923.7 person-years of follow-up (mean: 3.7 ± 0.7 years). The mean baseline MoCA was 19.5 ± 4.5 points, and the follow-up MoCA was 18.1 ± 4.9 points (P = 0.001). Overall, 154 individuals (61%) had lower MoCA scores at follow-up. The best fitted longitudinal linear model showed a decline of follow-up MoCA from baseline (β: 0.14; 95% CI, 0.0-0.21; P < 0.001). High glucose levels, global cortical atrophy, and white matter hyperintensities were independently and significantly associated with greater MoCA decline. CONCLUSION: This study provides evidence of cognitive decline in older adults living in a rural setting. Main targets for prevention should include glucose control and the control of factors that are deleterious for the development of cortical atrophy and white matter hyperintensities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".