The Estimation of Relative Fitness and Frailty in Community-Dwelling Older Adults Using Self-Report Data
Bibliographic record
Abstract
BACKGROUND: While on average health declines with age, it also becomes more variable with age. As a consequence of this marked variability, it becomes more important as people age to have a means of summarizing health status, but how precisely to do so remains controversial. We developed one measure of health status, personal biological age, from a frailty index. The index itself is a count of deficits derived, in the first instance, from a clinical database. In our earlier investigations, personal biological age demonstrated a strong relationship with 6-year survival. Here we extend this approach to self-reported data. METHODS: This is a secondary analysis of community-dwelling people aged 65 years and older (n = 9008) in the Canadian Study of Health and Aging. The frailty index was calculated from 40 self-reported variables, representing symptoms, attitudes, illnesses, and function. Personal biological age was estimated for each individual as the age corresponding to the mean chronological age for the index value. Individual frailty (and the related construct of fitness) was calculated as the difference between chronological and personal biological age. RESULTS: The frailty index showed, on average, an exponential increase with age at an average rate of 3% per year. Although women, on average, demonstrate more frailty than men of the same chronological age, their survival chances are greater. The frailty index strongly correlated (Pearson r =.992 for women and.955 for men) with survival. CONCLUSIONS: A frailty index, based on self-report data, can be used as a tool for capturing heterogeneity in the health status of older adults.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".