Social vulnerability and survival across levels of frailty in the Honolulu-Asia Aging Study
Bibliographic record
Abstract
PURPOSE: we evaluated mortality risk in relation to social vulnerability across levels of frailty among a cohort of older Japanese-American men. METHODS: in secondary analysis of the Honolulu-Asia Aging Study (HAAS), participants (n = 3,271) were aged 72-93 years at baseline. A frailty index (FI) created using 58 potential health deficits to quantify participants' frailty level at baseline, with four frailty strata: 0.0 < FI ≤ 0.1 (n = 1,074); 0.1 < FI ≤ 0.20 (n = 1,549); 0.2 < FI ≤ 0.30 (n = 472); FI > 0.3 (n = 176). Similarly, a social vulnerability index was created using 19 self-reported social deficits. Cox proportional hazard modelling was employed to estimate the impact of social vulnerability across the four levels of frailty, accounting for age, smoking, alcohol use and variation in health deficits within each frailty level. RESULTS: for the fittest participants, social vulnerability was associated with mortality (hazards ratio (HR) = 1.04, 95% confidence interval (CI) = 1.01, 1.07; P value = 0.008). Similarly, for those considered at risk for frailty, each social deficit was associated with a 5% increased risk of mortality. For frail individuals, the Cox regression analyses indicated that social vulnerability was not significantly associated with mortality (0.2 < FI ≤ 0.3: HR = 1.016, 95% CI = 0.98, 1.06; P value = 0.442; FI > 0.3: HR = 0.98, 95% CI = 0.93, 1.04). CONCLUSIONS: for the fittest and at-risk HAAS participants, the accumulation of social deficits was associated with significant increases in mortality risk. For frail individuals (FI > 0.20), the estimation of mortality risk may depend more so on intrinsic factors related to their health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".