Alzheimer's disease research and development: a call for a new research roadmap
Bibliographic record
Abstract
Epidemiological projections of the prevalence of Alzheimer's disease (AD) and related dementias, the rapidly expanding population over the age of 65, and the enormous societal consequence on health, economics, and community foretell of a looming global public health crisis. Currently available treatments for AD are symptomatic, with modest effect sizes and limited impact on longer term disease outcomes. There have been no newly approved pharmaceutical treatments in the last decade, despite enormous efforts to develop disease-modifying treatments directed at Alzheimer's-associated pathology. An unprecedented collaborative effort of government, regulators, industry, academia, and the community at-large is needed to address this crisis and to develop an actionable plan for rapid progress toward successfully developing effective treatments. Here, we map out a course of action in four key priority areas, including (1) addressing the fundamental mechanisms of disease, with the goal of developing a core set of research tools, a framework for data sharing, and creation of accessible validated and replicated disease models; (2) developing translational research that emphasizes rapid progress in disease model development and better translation from preclinical to clinical stages, deploying leading technologies to more accurately develop predictive models; (3) preventing AD through the development of robust methods and resources to advance trials and creating fundamental resources such as continuous adaptive trials, registries, data repositories, and instrument development; and (4) innovating public/private partnerships and global collaborations, with mechanisms to incentivize collaborations and investments, develop larger precompetitive spaces, and more rapid data sharing.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".