Frailty prevalence and factors associated with the Frailty Phenotype and Frailty Index: Findings from the North West Adelaide Health Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of frailty and associated factors in the North West Adelaide Health Study (2004-2006) using the Frailty Phenotype (FP) and Frailty Index (FI). METHODS: Frailty was measured in 909 community-dwelling participants aged ≥65 years using the FP and FI. RESULTS: The FP classified 18% of participants as frail and the FI 48%. The measures were strongly correlated (r = 0.76, P < 0.001) and had a kappa agreement of 0.38 for frailty classification, with 37% of participants classified as non-frail by the FP being classified as frail by the FI. Being older, a current smoker, and having multimorbidity and polypharmacy were associated with higher frailty levels by both tools. Female, low income, obesity and living alone were associated with the FI. CONCLUSION: Frailty prevalence was higher when assessed using the FI. Socioeconomic factors and other health determinants contribute to higher frailty levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it