Analysis of frailty and survival from late middle age in the Beijing Longitudinal Study of Aging
Bibliographic record
Abstract
BACKGROUND: Frailty in individuals can be operationalized as the accumulation of health deficits, for which several trends have been observed in Western countries. Less is known about deficit accumulation in China, the country with the world's largest number of older adults. METHODS: This study analyzed data from the Beijing Longitudinal Study of Aging, to evaluate the relationship between age and deficit accumulation in men and women and to evaluate the impact of frailty on mortality. Community dwelling people aged 55+ years at baseline (n = 3275) were followed every two to three years between 1992 and 2000, during which time 36% died. A Frailty Index was constructed using 35 deficits, drawn from a range of health problems, including symptoms, disabilities, disease, and psychological difficulties. RESULTS: Most deficits increased the eight-year risk of death and were more lethal in men than in women, although women had a higher mean level of frailty (Frailty Index = 0.11 ± 0.10 for men, 0.14 ± 0.12 for women). The Frailty Index increased exponentially with age, with a similar rate in men and women (0.038 vs. 0.039; r > 0.949, P < 0.01). A dose-response relationship was observed as frailty increased. CONCLUSIONS: A Frailty Index employed in a Chinese sample, showed properties comparable with Western data, but deficit accumulation appeared to be more lethal than in the West.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".