The comprehensive, customized, cost‐effective approach (CCCAP) to prevention of dementia
Bibliographic record
Abstract
The Food and Drug Administration's controversial approval of aducanumab has sounded a wake-up call. Is the search of a silver bullet to stop Alzheimer's disease the only way to prevent dementia? The controversies and costs have opened minds to alternative approaches, the most promising being that we are already preventing some dementias in some high-income countries but do not know yet how. This article proposes one way. It requires that the approach be (1) comprehensive, taking into account all relevant environmental, socioeconomic, and individual risk and protective factors; (2) customized, because contributing factors vary by region and among individuals; and (3) cost effective, implemented in actionable units. Savings of scale could occur by preventing stroke, heart disease, and dementia together. They share the same risk factors and pose risks for each other. Brain health could be the unifying, motivating, and actionable key to health, productivity, and well-being.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".