Insights into the use of biomarkers in clinical trials in Alzheimer's disease
Bibliographic record
Abstract
Biomarkers have been instrumental in population selection and disease monitoring in clinical trials of recently FDA-approved drugs targeting amyloid-β to slow the progression of Alzheimer's disease (AD). As new therapeutic strategies and biomarker techniques emerge, the importance of biomarkers in drug development is growing exponentially. In this emerging landscape, biomarkers are expected to serve a wide range of contexts of use in clinical trials focusing on AD and related dementias. The joint FDA-NIH BEST (Biomarkers, EndpointS, and other Tools) framework provides standardised terminology to facilitate communication among stakeholders in this increasingly complex field. This review explores various applications of biomarkers relevant to AD clinical trials, using the BEST resource as a reference. For simplicity, we predominantly provide contextual characterizations of biomarkers use from the perspective of drugs targeting amyloid-β and tau proteins. However, general definitions and concepts can be extrapolated to other targets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".