Joint Modeling of Biomarker Cascades Along An Unobserved Disease Progression with Differentiate Covariate Effects: An Application in Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's Disease (AD) research has shifted to focus on biomarker trajectories and their potential use in understanding the underlying AD-related pathological process. A conceptual framework was proposed in modern AD research that hypothesized biomarker cascades as a result of underlying AD pathology. In this paper, we leveraged this idea to jointly model AD biomarker trajectories as a function of the latent AD disease progression with individual and covariate effects in the latent disease progression model and the biomarker cascade. We tailored our methods to address a number of real-data challenges that are often present in AD studies. Simulation studies were performed to investigate the proposed approach under various realistic but less-than-ideal situations. Finally, we illustrated the methods using real data from the BIOCARD and the ADNI studies. The analyses investigated cascading patterns of AD biomarkers in these datasets and presented prediction results for individual-level profiles over time. These findings highlight the potential of the conceptual biomarker cascade framework to be leveraged for diagnosis and monitoring.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".