Dementia risk and prevention by targeting modifiable vascular risk factors
Bibliographic record
Abstract
The incidence of dementia is expected to double in the next 20 years and will contribute to heavy social and economic burden. Dementia is caused by neuronal loss that leads to brain atrophy years before symptoms manifest. Currently, no cure exists and extensive efforts are being made to mitigate cognitive impairment in late life in order to reduce the burden on patients, caregivers, and society. The most common type of dementia, Alzheimer's disease (AD), and vascular dementia (VaD) often co-exists in the brain and shares common, modifiable risk factors, which are targeted in numerous secondary prevention trials. There is a growing need for non-pharmacological interventions and infrastructural support from governments to encourage psychosocial and behavioral interventions. Secondary prevention trials need to be redesigned based on the risk profile of individual subjects, which require the use of validated and standardized clinical, biological, and neuroimaging biomarkers. Multi-domain approaches have been proposed in high-risk populations that target optimal treatment; clinical trials need to recruit individuals at the highest risk of dementia before symptoms develop, thereby identifying an enriched disease group to test preventative and disease modifying strategies. The underlying aim should be to reduce microscopic brain tissue loss by modifying vascular and lifestyle risk factors over a relatively short period of time, thus optimizing the opportunity for preventing dementia in the future. Collaboration between international research groups is of key importance to the optimal use and allocation of existing resources, and the development of new techniques in preventing dementia. This article is part of the Special Issue "Vascular Dementia".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".