Frailty prevalence and factors associated with the Frailty Phenotype and Frailty Index: Findings from the North West Adelaide Health Study
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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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".