Maximizing the Utility of Alzheimer's Disease Trial Data: Sharing of Baseline A4 and LEARN Data
Bibliographic record
Abstract
BACKGROUND: The Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies were conducted between 2014 and 2023, with enrollment completed in 2017 and final study results reported in 2023. The study screening process involved the collection of initial clinical, cognitive, neuroimaging, and genetic measures to determine eligibility. Once randomized, enrolled participants were assessed every four weeks over a 4.5-year follow-up period during which longitudinal clinical, cognitive, and neuroimaging measures were collected. A large number of longitudinal fluid biospecimens were also collected and banked. Consistent with the NIH data sharing policy and the principles of Open Science, the A4/LEARN investigators aimed to share data as broadly and early as possible while still protecting participant privacy and confidentiality and the scientific integrity of the studies. OBJECTIVES: We describe the approach, methods, and platforms used to share the A4 and LEARN pre-randomization study data for secondary research use. Preliminary results measuring the impact of these efforts are also summarized. We conclude with a discussion of lessons learned and next steps. DESIGN: The materials shared included de-identified quantitative and image data, analysis software, instruments, and documentation. SETTING: The A4 and LEARN Studies were conducted at 67 clinical trial sites in the United States, Canada, Japan, and Australia. PARTICIPANTS: The A4 study screened (n=6763), enrolled, and randomized (n=1169) participants between the ages of 65 and 85 with a blinded follow-up period of 240 weeks followed by an open-label period of variable length. The LEARN study screened and enrolled individuals (n=538) who were ineligible for the A4 study based on nonelevated measures of amyloid accumulation using positron emission tomography imaging (amyloid PET). MEASUREMENTS: We provide descriptive measures of the data shared and summarize the frequency, characteristics, and status of all data access requests submitted to date. We evaluate the scientific impact of the data-sharing effort by conducting a literature search to identify related publications. RESULTS: The A4 and LEARN pre-randomization study data were released in December 2018. As of May 8, 2024, 1506 requests have been submitted by investigators and citizen scientists from more than 50 countries. We identified 49 peer-reviewed publications that acknowledge the A4/LEARN study. CONCLUSIONS: Our initial results provide evidence supporting the feasibility and scientific utility of broad and timely sharing of Alzheimer's disease trial data.
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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 | 0.629 | 0.718 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.009 | 0.034 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".