INTERACTIONS BETWEEN FRAILTY AND SOCIAL VULNERABILITY ON THE SURVIVAL AND HEALTH TRANSITIONS OF OLDER ADULTS
Bibliographic record
Abstract
Abstract Frailty, an age-related state, is dominated by declining health over time, although stabilization and improvement can occur. We investigated how the accumulation of physical and social deficits interacts in health transitions and survival. Data from participants aged 65+ years in the Chinese Longitudinal Health Longevity Survey (CLHLS) 2005 cohort were analyzed (n=15613; women=57%; mean age=86.2±11.7). Participants were followed in 2008, 2011, 2014, and 2018, with their health and survival status reassessed each time. We constructed both a 47-item frailty index (FI) and a 26-item social vulnerability index (SVI). The FI included domains of morbidities, cognition, lifestyle, and disabilities; the SVI included World Health Organization-defined social determinants of health as – i.e., education, social security and inclusion, work/life/dwelling/services conditions, and early childhood development. At each follow-up, the FI strongly affected survival in a Logistic Regression model with baseline age, FI, SVI, and sex as covariates (e.g., a 1% FI increase for 14-year mortality: Odds Ratio OR=1.07, 95% CI=1.06-1.08). The SVI showed a marginally significant effect on 3-year survival (e.g., a 1% SVI increase, OR=1.01, 95% CI=1.00-1.01), and diminished at longer follow-ups. The FI also affected health transitions (Figure 1A), as did the SVI, albeit to a lesser extent (Figure 1B). In health transitions and survival of older adults, frailty increases risk and often interacts with social vulnerability. Our study underscores why we should characterize both physical and social factors to understand health and its dynamics in aging.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".