Rural residence and risk of dementia.
Bibliographic record
Abstract
INTRODUCTION: We sought to determine whether residence in a rural region is associated with a higher risk of dementia and a higher risk of developing dementia over a 5-year period than residence in an urban region. METHODS: This was a secondary analysis of a prospective cohort study. In 1991 and 1992, 1751 adults aged 65 years and older and residing in the community were sampled from a representative population-based registry, which included the entire province (time 1). Follow-up occurred 5 years later (time 2). Age, sex and education were selfreported. Rurality was determined by the population of the Census subdivision, with a population greater than 19 999 considered urban. Cognition was assessed using the Modified Mini-Mental State Examination, with those scoring below 78 invited to undergo a clinical examination to determine the presence of dementia. Cross-sectional analyses were conducted for participants with complete data at time 1. Prospective analyses were conducted for participants with normal cognition at time 1, who had complete data and survived until time 2. Logistic regression models were constructed for the outcome of dementia at times 1 and 2. RESULTS: Residence in a rural region was not associated with dementia in the cross-sectional analyses (adjusted odds ratio [OR] 1.08, 95% confidence interval [CI] 0.61-1.91) and did not predict dementia 5 years later (adjusted OR 1.05, 95% CI 0.66-1.68). CONCLUSION: We found no difference in the risk of dementia among older adults living in urban and rural regions of Manitoba.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".