Frailty, fitness and late-life mortality in relation to chronological and biological age
Bibliographic record
Abstract
BACKGROUND: People age at remarkably different rates, but how to estimate trajectories of senescence is controversial. METHODS: In a secondary analysis of a representative cohort of Canadians aged 65 and over (n = 2914) we estimated a frailty index based on the proportion of 20 deficits observed in a structured clinical examination. The construct validity of the index was examined through its relationship to chronological age (CA). The criterion validity was examined in its ability to predict mortality, and in relation to other predictions about aging. From the frailty index, relative (to CA) fitness and frailty were estimated, as was an individual's biological age. RESULTS: The average value of the frailty index increased with age in a log-linear relationship (r = 0.91; p < 0.001). In a Cox regression analysis, biological age was significantly more highly associated with death than chronological age. The average increase in the frailty index (i.e. the average accumulation of deficits) amongst those with no cognitive impairment was 3 per cent per year. CONCLUSIONS: The frailty index is a sensitive predictor of survival. As the index includes items not traditionally related to adverse health outcomes, the finding is compatible with a view of frailty as the failure to integrate the complex responses required to maintain function.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".