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 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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".