EXAMINING FRAILTY SUBDOMAINS (PHYSICAL, PSYCHOLOGICAL, COGNITIVE, SOCIAL) IN COMMUNITY-LIVING ADULTS 45–85
Bibliographic record
Abstract
Abstract Frailty prevalence estimates vary considerably (4.0% to 59.1% in one review). Understanding these differences and what drives frailty among individuals could lead to more focused interventions. Breaking frailty into subdomains and exploring their relationships is one approach to this. A 127-item overall Frailty Index (FI) based on data collected on Canadian Longitudinal Study on Aging (CLSA) Comprehensive Cohort participants (n = 30,097 community-living adults aged 45-85 at baseline) was used to create physical, psychological, cognitive, and social subdomain-specific FIs. Each FI was divided into quintiles with the highest 20% (Q5) considered the frailest. We assessed subdomain FI quintile concordance (Goodman and Kruskal’s gamma), described the joint distribution of those most frail (Q5), and estimated the association between sex and age with Q5 subdomain FIs membership (logistic regression). The concordance among FI subdomain quartiles was low. The highest gammas (0.25-0.36) were between the physical/psychological and psychological/cognition subdomains. Concordance was generally higher in females who had higher odds of Q5 membership for overall (1.5), physical (1.7), psychological (1.6) and social frailty (1.1), and lower odds for cognitive frailty (0.7). The odds of Q5 membership increased with age for overall frailty and all subdomains except psychological frailty, which decreased with age. Our results suggest low concordance among frailty subdomains and the relationship between Q5 membership with sex and age differed by subdomain. These data may help to better target frailty interventions, but longitudinal data are needed to explore both the time course and interrelationships across subdomains.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".