Implications of Risk Factors for Alzheimer’s Disease in Canada’s Indigenous Population
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
BACKGROUND: Indigenous peoples in Canada have higher prevalence of modifiable risk factors for Alzheimer's disease (AD). The relative importance of these risk factors on AD risk management is poorly understood. METHODS: Relative risks from literature and prevalence of risk factors from Statistics Canada or the First Nations Regional Health Survey were used to determine projected population attributable risk (PAR) associated with modifiable risk factors for AD (low education and vascular risk factors) among on- and off-reserve Indigenous and non-Indigenous people in Canada using the Levin formula. RESULTS: Physical inactivity had the highest PAR for AD among Indigenous and non-Indigenous peoples in Canada (32.5% [10.1%-51.1%] and 30.5% [9.2%-48.8%] respectively). The PAR for most modifiable risk factors was higher among Indigenous peoples in Canada, particularly among on-reserve groups. The greatest differences in PAR were for low educational attainment and smoking, which were approximately 10% higher among Indigenous peoples in Canada. The combined PAR for AD for all six modifiable risk factors was 79.6% among on-reserve Indigenous, 74.9% among off-reserve Indigenous, and 67.1% among non-Indigenous peoples in Canada. (All differences significant to p < .001.). CONCLUSIONS: Modifiable risk factors are responsible for the most AD cases among Indigenous peoples in Canada. Further research is necessary to determine the prevalence of AD and the impact of risk factor modification among Indigenous peoples in Canada.
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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 | 0.000 | 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 it