Relationships between socio‐clinico‐demographic factors and global cognitive function in the oldest old living in the Tokyo Metropolitan area: Reanalysis of the Tokyo Oldest Old Survey on Total Health (TOOTH)
Bibliographic record
Abstract
BACKGROUND: Despite a steady increase in life expectancy, a few studies have investigated cross-sectional correlates and longitudinal predictors of cognitive function, a core domain of the successful aging, among socio-clinico-demographic factors in the oldest-old exclusively. OBJECTIVES: The aims of this study were to examine socio-clinico-demographic characteristics associated with global cognition and its changes in the oldest-old. METHODS: We reanalyzed a dataset of cognitively preserved community-dwelling subjects aged 85 years and older in the Tokyo Oldest Old Survey on Total Health, a 6-year longitudinal observational study. This study consisted of (1) baseline cross-sectional analyses examining correlates of global cognition (n = 248) among socio-clinico-demographic factors and (2) longitudinal analyses examining baseline predictors for changes of global cognition in 3-year follow-up (n = 195). The Mini-Mental State Examination was used as a screening test to assess global cognition. RESULTS: At baseline, higher weights were related to higher cognitive function in the oldest-old. The baseline predictors of global cognitive decline in 3-year follow-up were higher global cognition, shorter education period, and lower sociocultural activities and lower instrumental activity of daily living, in this order. CONCLUSIONS: The present study suggests that it is crucial to attain higher education during early life and avoid leanness or obesity, participate in sociocultural cognitive activities during late life, and maintain instrumental activity of daily living to preserve optimal cognitive function in the oldest-old, which will facilitate developing prevention strategies for cognitive decline and promoting successful aging in this increasing 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".