Longitudinal follow up of dementia prevalence and risk in First Nations Australians living in the Torres Strait
Bibliographic record
Abstract
BACKGROUND: Previous research by the team identified a threefold prevalence of dementia in First Nations Australia living in the Torres Strait aged 45 and over. A total of 274 people participated in the study with chronic disease being a key dementia risk factor. Based on the Lancet Commission modelling, we used the prevalence study data to model the population attributable risk for dementia in the Torres Strait. Findings suggested almost 40% of risk were associated with modifiable risk factors. In order to better understand these risk factors, a longitudinal follow has been completed with the original cohort ten years later. METHOD: Original participants in the prevalence study were invited to have a comprehensive geriatric review. This included a diagnostic medical examination assessing physical, cognitive, and psychosocial factors together with culturally appropriate cognitive screening. Data were also collected on existing and emerging risk factors and associated problems of ageing including hearing, vision, frailty, depression and anxiety, nutrition, pain, sleep, and quality of life. Participants were classified as having normal cognition, Cognitive Impairment No Dementia (CIND) or dementia. RESULT: With over 80% of the sample reviewed, results to date show a high mortality rate, with 50% of the sample deceased. Within the remaining sample, 15% had progressed from normal cognitive status to either CIND or dementia. Of those classified as CIND previously, 4% remained stable with CIND and 10% progressed to dementia. 85% of the original sample of those diagnosed with dementia are now deceased. There were no significant age differences between groups (p> .05) highlighting the impact of dementia and cognitive impairment within younger groups in First Nations communities. Modifiable risk factors including vascular risk factors were significantly associated with cognitive impairment (p> .05). CONCLUSION: Understanding the fundamental factors influencing dementia risk in First Nations in Australia is essential to enable the team to develop and test culturally appropriate interventions and preventative strategies that will reduce the burden of chronic disease and geriatric conditions in First Nations Australians.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".