Assessing the measurement properties of a Frailty Index across the age spectrum in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Frailty is a way to appreciate the variable vulnerability to declining health status of people as they age. No consensus for measuring frailty has been established. This study aimed to adapt a Frailty Index (FI) to the Canadian Longitudinal Study on Aging (CLSA) and evaluate its applicability in both younger and older adults. METHODS: An FI was created based on 90 potential health deficits collected from adults aged 45-85 years at recruitment (N=21 241, 49.0% male). The construct validity of this instrument and the factor structure of the health deficits were evaluated. RESULTS: hypotheses for construct validity. FI values were significantly associated with age (r=0.17; p<0.001), falls (r=0.12; p<0.001), injuries (r=0.12; p<0.001), formal home care (r =0.30; p<0.001), informal home care (r=0.32; p<0.001) and use of assistive devices (r=0.40; p<0.001). Values were negatively associated with male sex (r=-0.12; p<0.001), income (r=-0.34; p<0.001) and education (r=-0.17; p<0.001). Key factors among the health indicators were physical functioning, satisfaction with life and depressive symptoms. Results did not change when the sample was stratified by age and sex. CONCLUSION: The FI is a feasible method to evaluate frailty and capture frailty-related heterogeneity in populations aged 45-85 years. In this study, the FI had good construct validity in middle-aged and older adults, showing expected correlations with sociodemographic factors consistently across age groups. This method can be easily reproduced in similar datasets, making the FI a generalisable instrument.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".