A limit to frailty in very old, community-dwelling people: a secondary analysis of the Chinese longitudinal health and longevity study
Bibliographic record
Abstract
BACKGROUND: it has been observed that a frailty index (FI) is limited by the value of 0.7. Whether this holds in countries with higher mortality rates is not known. OBJECTIVES: to test for and quantify a limit in very old Chinese adults and to relate mortality risk to the FI. DESIGN: secondary analysis of four waves (1998, 2000, 2002 and 2005) of the Chinese Longitudinal Health and Longevity Study (CLHLS). SUBJECTS: a total of 6,300 people from 22 of 31 provinces in China, aged 80-99 years at baseline and followed up to 7 years. METHODS: an FI was calculated as the ratio of actual to 38 possible health deficits. Frequency distributions were used to evaluate the limit to the FI. Logistic regression and survival analysis were used to evaluate the relationship between the FI and mortality. RESULTS: at each wave, a 99% submaximal limit to frailty was observed at FI = 0.7, despite consecutive losses to death. The death rate for those who were healthiest at baseline (i.e. those in whom the baseline FI = 0) increased from 0.18 at the 2-year follow-up to 0.69 by 7 years. At each wave, 100% mortality at 2 years was observed at FI close to 0.67. A baseline FI >0.45 was associated with 100% 7-year mortality. CONCLUSIONS: a limit to frailty occurred with FI = 0.7 which was not exceeded at any age or in any wave. There appears to be a demonstrable limit to the number of health problems that people can tolerate.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".