Predictors of maintaining cognitive function in older adults
Bibliographic record
Abstract
BACKGROUND: Although several risk factors for cognitive decline have been identified, much less is known about factors that predict maintenance of cognitive function in advanced age. METHODS: We studied 2,509 well-functioning black and white elders enrolled in a prospective study. Cognitive function was measured using the Modified Mini-Mental State Examination at baseline and years 3, 5, and 8. Random effects models were used to classify participants as cognitive maintainers (cognitive change slope > or = 0), minor decliners (slope < 0 and > 1 SD below mean), or major decliners (slope < or = 1 SD below mean). Logistic regression was used to identify domain-specific factors associated with being a maintainer vs a minor decliner. RESULTS: Over 8 years, 30% of the participants maintained cognitive function, 53% showed minor decline, and 16% had major cognitive decline. In the multivariate model, baseline variables significantly associated with being a maintainer vs a minor decliner were age (odds ratio [OR] = 0.65, 95% confidence interval [CI] 0.55-0.77 per 5 years), white race (OR = 1.72, 95% CI 1.30-2.28), high school education level or greater (OR = 2.75, 95% CI 1.78-4.26), ninth grade literacy level or greater (OR = 4.85, 95% CI 3.00-7.87), weekly moderate/vigorous exercise (OR = 1.31, 95% CI 1.06-1.62), and not smoking (OR = 1.84, 95% CI 1.14-2.97). Variables associated with major cognitive decline compared to minor cognitive decline are reported. CONCLUSION: Elders who maintain cognitive function have a unique profile that differentiates them from those with minor decline. Importantly, some of these factors are modifiable and thus may be implemented in prevention programs to promote successful cognitive aging. Further, factors associated with maintenance may differ from factors associated with major cognitive decline, which may impact prevention vs treatment strategies.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".