Levels of frailty and frailty progression in older urban- and regional-living First Nations Australians
Bibliographic record
Abstract
OBJECTIVES: To explore the prevalence of frailty, association between frailty and mortality, and transitions between frailty states in urban- and regional-living First Nations Australians. STUDY DESIGN: Secondary analysis of longitudinal data from the Koori Growing Old Well Study. First Nations Australians aged 60 years or more from five non-remote communities were recruited in 2010-2012 and followed up six years later (2016-2018). Data collected at both visits were used to derive a 38-item Frailty Index (FI). The FI (range 0-1.0) was classified as robust (<0.1), pre-frail (0.1- < 0.2), mildly (0.2- < 0.3), moderately (0.3- < 0.4) or severely frail (≥0.4). MAIN OUTCOME MEASURES: Association between frailty and mortality, examined using logistic regression and transitions in frailty (the percentage of participants who changed frailty category) during follow-up. RESULTS: At baseline, 313 of 336 participants (93 %) had sufficient data to calculate a FI. Median FI score was 0.26 (interquartile range 0.21-0.39); 4.79 % were robust, 20.1 % pre-frail, 31.6 % mildly frail, 23.0 % moderately frail and 20.5 % severely frail. Higher baseline frailty was associated with mortality among severely frail participants (adjusted odds ratio 7.11, 95 % confidence interval 2.51-20.09) but not moderately or mildly frail participants. Of the 153 participants with a FI at both baseline and follow-up, their median FI score increased from 0.26 to 0.28. CONCLUSIONS: Levels of frailty in this First Nations cohort are substantially higher than in similar-aged non-Indigenous populations. Screening for frailty before the age of 70 years may be warranted in First Nations Australians. Further research is urgently needed to determine the factors that are driving such high levels of frailty and propose solutions to prevent or manage frailty in this population.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".