Health Care Use and Physical, Psychological, Cognitive, and Social Frailty in Community-Living Adults 45-85
Bibliographic record
Abstract
Abstract This study examines associations between physical, psychological, social, and cognitive frailty domains with health care utilization (HCU) and the potential moderating effect of the last three domains on the association between physical frailty and HCU. A 127-item Frailty Index (FI) developed for the Canadian Longitudinal Study on Aging comprehensive cohort (n = 30,097) was used to create physical, psychological, cognitive, and social domain-specific FIs. Each FI was divided into quintiles with the highest 20% representing the frailest. Logistic regression was used to estimate unadjusted and adjusted (covariates: sex, age, income, smoking, physical activity, nutrition, and participation restriction) ORs (aORs) for frailty domains and HCU (formal/informal care, family physician visits, hospitalizations) and interactions between physical frailty and the other frailty domains. Physical frailty was associated with the highest HCU ORs in unadjusted (1.53 to 2.38) and adjusted (1.28 to 1.78) analyses, with the largest aOR for formal care (1.78, 95% CI 1.66, 1.91). For all HCU except formal care, the upper CI limits for social frailty were < 1, indicating those with higher levels of social frailty were less likely to use these services. Interactions between physical frailty and the other frailty domains were significant for only formal and informal care, with the aOR magnitudes for the other domains increasing with the level of physical frailty. Our data suggests that the drivers of HCU are multifactorial and the need to consider both frailty beyond physical characteristics and the complex relationships between frailty domains and HCU when assessing the outcomes of frailty interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".