EVIDENCE FOR THE LATENT FACTOR STRUCTURE OF FRAILTY
Bibliographic record
Abstract
Despite increasing demand to support ‘aging in community’ for frail seniors, there is no gold standard frailty measure to guide frailty assessments by health professionals. Current frailty measures are not sensitive enough to support effective screening and thus negatively impact health professional decision-making during their care of seniors living in the community. The aim of this study is to investigate the latent structure of frailty to inform refinement of existing frailty measures for seniors living in the community to develop a robust measurement tool. Using data from Canadians ≥ 65 who were participants in the national Canadian Longitudinal Study on Aging (CLSA) (2012–2015), we assessed factors for the latent structure of three frailty scales (Rockwood’s Frailty Index, Fried’s Frailty Criteria and the Edmonton Frailty Scale). Using structural equation modelling we explored the relationship between frailty and factors across physical, psychological, social, and clinical domains. Structural equation models were developed to identify factors for the latent structure of frailty. Our models (n=30,111) highlight several key factors common among the three frailty scales including: age, sex, dementia, Activities of Daily Living (ADL), Instrumental ADL’s (IADL), and cognition. Robust frailty assessments are key to effective health professional decision-making in support of ‘aging in community’ initiatives.
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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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".