Bibliographic record
Abstract
Biomarkers, both biological and imaging, are indicators of specific changes that characterize Alzheimer's disease (AD) progression in vivo. Knowing the precise relationship between biomarkers and disease severity would allow for accurate disease staging and possible forecasting of decline. Jack et al. suggested as an initial hypothesis that this relationship be sigmoidal; the objective of this article is to determine, using large-scale population data from ADNI, the precise shape of this association. We considered six different models (linear; quadratic; robust quadratic; local quadratic regression; penalized B-spline; and sigmoid) and used the Akaike Information Criterion to gauge how well these models compare in conforming to the data. We included 576 subjects (229 controls, 193 AD, and 154 mild cognitive impairment subjects who converted to AD) from the ADNI study, for whom baseline data on cerebrospinal fluid amyloid-β (Aβ)42, phosphorylated tau (p-tau), and total-tau (t-tau), hippocampal volumes, and FDG-PET were available. Analysis of this cross-sectional dataset showed that a local quadratic regression model was 42% more likely than a sigmoid to be the best model for Aβ42. This ratio augments to 22% and 73% for Penalized B-Spline in the case of p-tau and t-tau, respectively; to 3500% for the linear model for FDG-PET; and to 6700% for the Penalized B-Spline for hippocampal volumes. Preliminary, cross-sectional evidence therefore indicates that the shape of the association with disease severity is non-linear and differs between biomarkers.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".