Cumulative impact of health deficits, social vulnerabilities, and protective factors on cognitive dynamics in late life: a multistate modeling approach
Bibliographic record
Abstract
INTRODUCTION: Many factors influence late-life cognitive changes, and evaluating their joint impact is challenging. Typical approaches focus on average decline and a small number of factors. We used multistate transition models and index variables to look at changes in cognition in relation to frailty (accumulation of health deficits), social vulnerability, and protective factors in the Honolulu-Asia Aging Study (HAAS). METHODS: The HAAS is a prospective cohort study of 3,845 men of Japanese descent, aged 71 to 93 years at baseline. Cognitive function was measured using the Cognitive Abilities Screening Instrument (CASI). Baseline index variables were constructed of health deficits (frailty), social vulnerabilities, and protective factors. The chances of improvement/stability/decline in cognitive function and death were simultaneously estimated using multistate transition modeling for 3- and 6-year transitions from baseline. RESULTS: On average, CASI scores declined by 5.3 points (standard deviation (SD) = 10.0) over 3 years and 9.5 points (SD = 13.9) over 6 years. After adjusting for education and age, baseline frailty was associated with an increased risk of cognitive decline at 3 years (β = 0.18, 95% confidence interval (CI), 0.08 to 0.29) and 6 years (β = 0.40, 95% CI, 0.27 to 0.54). The social vulnerability index was associated with 3-year changes (β = 0.16, 95% CI, 0.09 to 0.23) and 6-year changes (β = 0.14, 95% CI, 0.05 to 0.24) in CASI scores. The protective index was associated with reductions in cognitive decline over the two intervals (3-year: β = -0.16, 95% CI, -0.24 to -0.09; 6-year: β = -0.21, 95% CI, -0.31 to -0.11,). CONCLUSIONS: Research on cognition in late life needs to consider overall health, the accumulation of protective factors, and the dynamics of cognitive change. Index variables and multistate transition models can enhance understanding of the multifactorial nature of late-life changes in cognition.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".