White matter hyperintensity burden in elderly cohort studies: The Sunnybrook Dementia Study, Alzheimer's Disease Neuroimaging Initiative, and Three‐City Study
Bibliographic record
Abstract
Given the recent acknowledgement of the complex mixed pathologies that contribute to the clinical expression of dementia, various cohort studies have aimed to examine Alzheimer's disease and cerebrovascular disease as comorbid pathologies, with neuroimaging playing a central role in these studies. Using white matter hyperintensities (WMH) as a biomarker of cerebrovascular disease, we compared WMH burden between the Sunnybrook Dementia Study, the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), the Three-City Study, and various other studies around the world. Based on our findings, it was evident that ADNI-1 had minimal WMH burden relative to other large studies that examine aging and dementia. This low WMH burden in ADNI-1 may be considered as both an advantage, representing a relatively "pure" sample with little confounding vasculopathy, and a disadvantage, as it limits generalizability to "real-world" patient populations with mixed pathologies and to nondemented groups with baseline vascular disease. We explore possible reasons for this distinction, including management of vascular risk factors, gaps in diagnostic criteria, and future directions for clinical research.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".