P4‐644: A STRATEGIC HUMAN‐BASED ADRD RESEARCH PROGRAM: TOWARD BASIC MECHANISM DISCOVERY, THERAPEUTIC TRANSLATION, PUBLIC POLICY, EDUCATION AND HEALTH
Bibliographic record
Abstract
Despite repeated announcements of breakthroughs in Alzheimer's disease and related dementias (ADRD) research, the stark reality remains that there have been no substantial advancements in treatments. These failures in clinical translation are in large part due to a systemic dependence on a flawed framework based on validating preclinical findings using animal experimentation. In particular, researchers are repeatedly incentivized by funding programs toward dominant reductionist frameworks (e.g., amyloid-, tau-centric) and xenogenic research (e.g., transgenic and chimeric models). Here we present an integrated program of impact studies, research protocols and policy proposals that aim to address the stagnation in ADRD research, ethical issues and clinical outcomes by advancing new multi-scale human-based approaches. The framework includes four distinct areas and their concomitant methodologies: [1] basic research approaches [2] therapeutics [3] public policy and [4] education. In each case we highlight concrete examples of procedures that identity shortcomings and propose alternative approaches in each of the four domains. In the [1] basic research and [2] translational research domains we have identified widespread use of protocols that are contaminated with xenogenic materials thereby undermining basic findings and limiting clinical applicability. To this end, we describe the development of a xeno-free database initiative that can address these shortcomings. In addition, we outline the results of computational neural models that can help identify network-based factors in ADRD that go beyond reductionist cell-centered approaches. At the [3] policy domain we identify how institutes and funding agencies (e.g., NIH) can accelerate discovery and introduce guidelines for comprehensive human-based basic research and clinical funding policies and programs. Finally, we begin to address the need for [4] human-based research education in scientific training and clinical programs. As a concrete example, we showcase a novel human-based curriculum we are implementing at the graduate level. The need for improvements to individual and public health are inseparable from the need for broad-sweeping changes to existing ADRD research frameworks. By implementing integrated human-based laboratory, therapeutic, public policy, and educational approaches in basic science and clinical studies we can begin to address systemic factors contributing to the stagnation in ADRD research outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".