From Dementia to Eumentia: A New Approach to Dementia Prevention
Bibliographic record
Abstract
BACKGROUND: During the past 40 years, dementia prevention approaches have ranged from searching for a drug to prevent progression to Alzheimer's disease to preventing dementia through multidomain lifestyle interventions. Current Approaches: The search for a silver bullet has yielded good science but no clinical results. The multi-model lifestyle intervention approach has shown encouraging results. The largest proportion of resources in prevention have been devoted to finding a drug to prevent, mitigate, or delay what is being called "Alzheimer's disease of late onset." The reality is that Alzheimer's pathology is common among the elderly, but it seldom only occurs alone. The only treatable and preventable pathology currently is vascular. Hence arose the concept of "vascular cognitive impairment" meaning any vascular cause or risk factor associated with cognitive impairment. The majority of cases of cognitive impairment in the elderly have a vascular component that is treatable and preventable and identifiable by several means, including a simple ischemic score. CONCLUSION: Since environmental, socioeconomic, and individual risk factors contribute to dementia, we proposed a Comprehensive, Customized, Cost-effective APProach (the CCC-APP) implemented in actionable units with the focus on promoting brain health (eumentia). KEY MESSAGES: We should implement dementia prevention approaches in actionable units around optimal brain health or eumentia. Heart disease, stroke, and dementia share mostly the same risk and protective factors; thus, their joint prevention is desirable. We need a comprehensive, customized, and cost-effective approach to joint prevention of stroke, heart disease, and dementia. We call for literal and virtual meetings of researchers of all the relevant disciplines to work on operational definitions and interdisciplinary collaborations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".