THE A4 TRIAL: ANTI‐AMYLOID TREATMENT OF ASYMPTOMATIC ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Converging evidence suggests that approximately one-third of clinically normal older individuals demonstrate amyloid-b accumulation, associated with functional and structural imaging abnormalities, elevated CSF phospho-tau, and increased rate of cognitive decline, consistent with the preclinical stages of AD. The A4 study is a secondary prevention trial that will test the hypothesis that an anti-amyloid treatment can slow cognitive decline in amyloid-positive older individuals at high risk for progression to the symptomatic phases of AD. The Alzheimer's Disease Cooperative Study (ADCS) is partnering with Eli Lilly and Company to conduct a Phase 3, double-blind, placebo-controlled, 168–week trial of solanezumab in over 1000 clinically normal older individuals with evidence of amyloid-positivity on PET amyloid imaging with florbetapir. Eligible subjects will be ages 65-85 years, CDR 0, and MMSE 27-30, but may have subjective cognitive concerns. The primary outcome is a cognitive composite of episodic memory and executive function measures (ADCS-PACC). Exploratory outcomes include iPAD testing in collaboration with CogState, participant–reported outcomes, longitudinal amyloid PET imaging, functional connectivity and volumetric MRI, and CSF biomarkers in a subset. The A4 study also imbeds an ethics component to study the process of safely disclosing PET amyloid imaging results, and a natural history cohort called LEARN (Longitudinal Evaluation of Amyloid Risk and Neurodegeneration) to elucidate the risk of decline in “amyloid-negative” individuals compared with “amyloid-positive” individuals randomized to the A4 placebo arm. The A4 study will be conducted at 60 ADCS sites in the U.S., Canada, and Australia, and is funded by a public-private-philanthropic partnership. The A4 trial started enrollment in early 2014, and we will present an update on study progress. Based on power analyses from ongoing large biomarker cohorts, we should have adequate power with n=500 subjects per arm to detect a 30% treatment-related decrease in the rate of cognitive decline. The A4 trial will provide complementary information to the prevention studies being conducted in genetic-risk cohorts. The A4 study design may prove to be a valuable platform to test other anti-amyloid agents during the preclinical stages of AD, and ultimately combination therapies, to prevent progression to AD dementia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".