Optimizing the Aging Brain: The BEAD Study on the Ethics of Dementia Prevention
Bibliographic record
Abstract
Dementia has lately undergone a profound reconceptualization. Long conceived of as an unpreventable process of mental deterioration, current evidence shows that it can be prevented in at least one in three cases intervening on a specified set of factors. Issues of justice and equity loom large on the implementation of dementia prevention, from a global health perspective. Our project thus embraces emerging evidence about dementia risk factors and their uneven distribution nationally and globally by specifically focusing on the situated aspects of dementia prevention. The aim of the BEAD study (Optimizing the Aging Brain? Situating Ethical Aspects in Dementia Prevention) is to dissect the ethical and clinical assumptions of this novel understanding of dementia, and to analyze how such new discourse on dementia prevention plays out in three countries: Canada, Germany and Switzerland. This study adopts a multi-perspective, comparative, qualitative approach, combining stakeholder interviews with different kinds of focused ethnographies, elaborating on conceptual, ethical, and social aspects of what we would like to call the "new dementia". By situating the paradigmatic shifts in Alzheimer's and dementia research within current aging cultures and contemporary social policies, we aim to initiate a debate about the often implicit unresolved social, ethical, and political implications and preconditions of the medical understanding and handling of cognitive disorders.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.065 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".