Epidemiology and prevalence of Alzheimer's disease and risk factors
Bibliographic record
Abstract
An estimation of the evolving prevalence of dementia remains difficult for two main reasons.There is the problem of the underdiagnosis of dementia in Europe, particularly in France.It is also difficult in a crosssectional study to document a decline in cognitive function and an impairment in the ability to be involved in daily living activities, particularly in people with a very low level of education, living alone or confined to an institution.Therefore, a longitudinal study is the most accurate type of study to estimate the prevalence of dementia.The best estimation of the prevalence of dementia is given by a meta-analysis of the European longitudinal studies on dementia published in 2000. 1 In these studies, the estimation of the prevalence of dementia reaches 6.3% after the age of 65 years.The most frequent cause is Alzheimer's disease (AD) (4.3%), followed by mixed vascular dementia (1.5%). 2 The risk of dementia increases with age and is higher in women.This last observation can be explained by the difference in survival rates between men and women with dementia and AD.However, the incidence value of dementia represents the real risk of the disease in the population.The risk of dementia and AD is higher in women than in men, but the risk of vascular dementia is more significant in men (Table 1). 3 This difference between the sexes is not observed in the USA or Canada.It could reflect a difference in life expectancy between men and women, which is much higher in
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".