Alzheimer's disease with cerebrovascular disease: current status in the Asia–Pacific region
Bibliographic record
Abstract
BACKGROUND: There is growing awareness of the coexistence of Alzheimer's disease and cerebrovascular disease (AD+CVD), however, due to lack of well-defined criteria and treatment guidelines AD+CVD may be underdiagnosed in Asia. METHODS: Sixteen dementia specialists from nine Asia Pacific countries completed a survey in September 2014 and met in November 2014 to review the epidemiology, diagnosis and treatment of AD+CVD in Asia. A consensus was reached by discussion, with evidence provided by published studies when available. RESULTS: AD accounts for up to 60% and AD+CVD accounts for 10-20% of all dementia cases in Asia. The reasons for underdiagnosis of AD+CVD include lack of awareness as a result of a lack of diagnostic criteria, misdiagnosis as vascular dementia or AD, lack of diagnostic facilities, resource constraints and cost of investigations. There is variability in the tools used to diagnose AD+CVD in clinical practice. Diagnosis of AD+CVD should be performed in a stepwise manner of clinical evaluation followed by neuroimaging. Dementia patients should be assessed for cognition, behavioural and psychological symptoms, functional staging and instrumental activities of daily living. Neuroimaging should be performed using computed tomography or magnetic resonance imaging. The treatment goals are to stabilize or slow progression as well as to reduce behavioural and psychological symptoms, improve quality of life and reduce disease burden. First-line therapy is usually an acetylcholinesterase inhibitor such as donepezil. CONCLUSION: AD+CVD is likely to be under-recognised in Asia. Further research is needed to establish the true prevalence of this treatable and potentially preventable disease.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".