Frailty state transitions and associated factors in South Australian older adults
Bibliographic record
Abstract
AIM: Frailty is a state of decreased physiological reserve and vulnerability to stressors. Understanding the characteristics of those most at risk of worsening, or likely to improve their frailty status, are key elements in addressing this condition. The present study measured frailty state transitions and factors associated with improvement or worsening frailty status in the North West Adelaide Health Study. METHODS: Frailty was measured using the frailty phenotype (FP) and a 34-item frailty index (FI) for 696 community-dwelling participants aged ≥65 years, with repeated measures at 4.5-year follow up. RESULTS: Improvement in frailty state was common for both tools (FP 15.5%; FI 7.9%). The majority remained stable (FP 44.4%; FI 52.6%), and many transitioned to a worse level of frailty (FP 40.1%; FI 39.5%). For both measures, multimorbidity was associated with worsening frailty among non-frail participants. Among pre-frail participants, normal waist circumference was associated with improvement, whereas older age was associated with worsening of frailty status. Among frail individuals, younger age was associated with improvement, and male sex and older age were associated with worsening frailty status. CONCLUSIONS: Frailty is a dynamic process where improvement is possible. Multimorbidity, obesity, age and sex were associated with frailty transitions for both tools. Geriatr Gerontol Int 2018; 18: 1549-1555.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".