Accepter May 17, 2004 Mechanisms of Aging and Development CHANGES WITH AGE IN THE DISTRIBUTION OF A FRAILTY INDEX
Bibliographic record
Abstract
Models of human mortality include a factor that summarizes intrinsic differences in individual rates of aging, commonly called frailty. Frailty also describes a clinical syndrome of apparent vulnerability. In a representative, cross-sectional, Canadian survey (n=66,589) we calculated a frailty index as the mean accumulation of deficits and previously showed it to increase exponentially with age. Here, its density function exhibited a monotonic change in shape, being least skewed at the oldest ages. Although the shape gradually changed, the frailty index was well fitted by a gamma distribution. Of note, the variation coefficient, initially high, decreased from middle age on. Being able to quantify frailty means that health risks can be summarized at both the individual and group levels. Keywords: Ageing, Frailty index, Gamma density, Heterogeneity. Living systems, including humans, age at different rates, so that concepts such a “longevity factor” (Beard, 1973) and “frailty ” (Vaupel et al., 1979) have been invoked, although without details on how they might be assessed. We have argued that a “frailty index ” which relates the accumulation of deficits to time, corresponds to clinical assessments of the relative fitness of people of the same chronological age (Mitinitski et al., 2001; Rockwood et al., 2002). The frailty index demonstrated an exponential increase
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.640 | 0.300 |
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".