Amyloid status imputed from a multimodal classifier including structural MRI distinguishes progressors from nonprogressors in a mild Alzheimer's disease clinical trial cohort
Bibliographic record
Abstract
INTRODUCTION: Mild-Alzheimer's disease (AD) subjects without significant Aβ pathology represent a confounding finding for clinical trials because they may not progress clinically on the expected trajectory, adding variance into analyses where slowing of progression is being measured. METHODS: A prediction model based on structural magnetic resonance imaging (MRI) in combination with baseline demographics and clinical measurements was used to impute Aβ status of a placebo-treated mild-AD sub-cohort (N = 385) of patients participating in global phase 3 trials. The clinical trajectories of this cohort were evaluated over 18 months duration of the trial, stratified by imputed Aβ status within a mixed-model repeated measures statistical framework. RESULTS: In the imputed Aβ-positive cohort, both cognitive (ADAS-Cog14 and MMSE) and functional (ADCS-iADL) measures declined more rapidly than in the undifferentiated population. DISCUSSION: Our results demonstrate imputing Aβ status from MRI scans in mild-AD subjects may be a useful screening tool in global clinical trials if amyloid measurement is not available.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".