Risk factors for Alzheimer's disease: a population-based, longitudinal study in Manitoba, Canada
Bibliographic record
Abstract
Background Current knowledge of risk factors for Alzheimer's disease (AD) is limited. Data from a longitudinal, population-based study of dementia in Manitoba, Canada were used to investigate risk factors for AD. Methods Cognitively intact subjects completed a risk factor questionnaire assessing sociodemographic, genetic, environmental, medical and lifestyle exposures. Five years later, 36 subjects had developed AD and 658 remained cognitively intact. Results Older subjects or those who had fewer years of education were at greater risk of AD. After adjusting for age, education and sex, occupational exposure to fumigants/ defoliants was a significant risk factor for AD (relative risk [RR] = 4.35; 95% CI : 1.05–17.90). A history of migraines increased the risk of AD (RR = 3.49; 95% CI : 1.39–8.77); an even stronger effect was noted among women. Self-reported memory loss at baseline was associated with subsequent development of AD (RR = 5.15; 95% CI : 2.36–11.27). Vaccinations and occupational exposure to excessive noise reduced the risk of AD. Conclusions Some well-known risk factors for AD were confirmed in this study and potential new risk factors were identified. The association of AD with a history of migraines and occupational exposure to defoliants/fumigants is of particular interest because these are biologically plausible risk factors. KEY MESSAGES Potential new risk factors for Alzheimer's disease were identified. Migraines and occupational exposure to defoliants/fumigants appear to increase the risk of Alzheimer's disease and are of particular interest because they are biologically plausible risk factors. The potential effect on data quality of memory problems among cognitively intact subjects should be considered in the design and analysis of risk factor studies of Alzheimer's disease.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".