Regional Variability in the Prevalence of Cerebral White Matter Lesions: An MRI Study in 9 European Countries (CASCADE)
Bibliographic record
Abstract
White matter lesions (WML) on MRI of the brain are common in both demented and nondemented older persons. They may be due to ischemic events and are associated with cognitive and physical impairments. It is not known whether the prevalence of these WML in the general population differs across European countries in a pattern similar to that seen for coronary heart disease. Here we report the prevalence of WML in 1,805 men and women drawn from population-based samples of 65- to 75-year-olds in ten European cohorts. Data were collected using standardized methods as a part of the multicenter study CASCADE (Cardiovascular Determinants of Dementia). Centers were grouped by region: south (Italy, Spain, France), north (Netherlands, UK, Sweden), and central (Austria, Germany, Poland). In this 10-year age stratum, 92% of the sample had some lesions, and the prevalence increased with age. The prevalence of WML was highest in the southern region, even after adjusting for differences in demographic and selected cardiovascular risk factors. Brain aging leading to disabilities will increase in the future. As a means of hypothesis generation and for health planning, further research on the geographic distribution of WML may lead to the identification of new risk factors for these lesions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".