Bibliographic record
Abstract
Abstract The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has made many important contributions to the development of Alzheimer’s Disease (AD) disease modifying treatments and diagnostic biomarkers. Since its funding in 2004 by the National Institutes of Aging, the goal of ADNI has been the validation of biomarkers for AD treatment trials. ADNI has enrolled over 2,400 participants in the USA and Canada for longitudinal clinical, cognitive, and biomarker studies. A major accomplishment is that ADNI data has been widely used for the design and of Phase 2 and 3 clinical trials, including Aducanumab, Lecanemab, and Donanemab. In addition, ADNI first demonstrated the feasibility of multisite amyloid PET scans leading to FDA approval for amyloid imaging. ADNI MRI and PET standardized protocols, widely used by academe and industry, have allowed the efficient collection of large image databases, with many thousands of MRI and PET scans. ADNI demonstrated the feasibility of multisite lumbar punctures and validated CSF amyloid and tau, leading to FDA approved CSF diagnosis. ADNI demonstrated relationships between clinical decline and amyloid, the role of tau in driving cognitive decline, showing the feasibility of prevention studies such as A4, AHEAD, and TrailblazerAlz3. ADNI provides all de‐identified data to the scientific community without embargo through the ADNI website adni.loni.usc.edu, leading to over 5,500 publications. The ADNI public‐private partnership model for large multisite studies provided the inspiration for many studies including: DIAN, PPMI, ALL FDT, 4RTNI, LEADS and others. ADNI’s Diversity Task Force greatly increased enrollment of under‐represented participants. Lack of inclusion of under‐represented people (especially Black and Latino adults) is a major problem for clinical trials. Our current goal is to enroll at least 50% of new participants from under‐represented groups using a culturally engaged approach with digital marketing campaigns, web‐based screening, and remote blood collection for plasma AD biomarkers. ADNI will continue to develop AD clinical trials for the future, leading to the prevention of AD symptoms and 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 | 0.067 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.046 | 0.038 |
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".