Prevalence, risk factors and consequences of cerebral small vessel diseases: data from three Asian countries
Bibliographic record
Abstract
BACKGROUND: Cerebral small vessel disease (SVD) has been suggested to be more common in Asians compared with Caucasians. However, data from population-based studies in Asia are lacking. We report on the prevalence, risk factors and consequences of SVD from contemporary studies in three Asian countries using 3-Tesla MRI for the evaluation of SVD. METHODS: Clinical, cognitive and 3-Tesla brain MRI assessments were performed among participants of three studies from Singapore, Hong Kong and Korea. SVD markers include white matter hyperintensities (WMHs) using the modified Fazekas scale, lacunes and microbleeds. Cognition was assessed using the Mini Mental Status Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Adjustments were made for age, sex and cardiovascular risk factors. RESULTS: A total of 1797 subjects were available for analysis (mean age: 70.1±6.3 years and 57% women). The prevalence of confluent WMH was 36.6%, lacunes, 24.6% and microbleeds, 26.9%. Presence of all three SVD markers showed a steeper increase with increasing age rising from 1.9% in the lowest to 46.2% in the highest 5-year age strata. The major risk factors for the increased severity of SVD markers were advancing age and hypertension. Moreover, increasing severity of SVD markers was independently associated with worse performance on MMSE and MoCA. CONCLUSION: Elderly Asians have a high burden of SVD which was associated with cognitive dysfunction. This suggests that SVD markers should be a potential target for treatment in clinical trials so as to delay progression of cerebrovascular disease and potentially cognitive decline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".