ADNI Biomarker Core: A review of progress since 2004 and future challenges
Bibliographic record
Abstract
BACKGROUND: We describe the Alzheimer's Disease Neuroimaging Initiative (ADNI) Biomarker Core major activities from October 2004 to March 2024, including biobanking ADNI cerebrospinal fluid (CSF), plasma, and serum biofluid samples, biofluid analyses for Alzheimer's disease (AD) biomarkers in the Biomarker Core and various non-ADNI laboratories, and continuous assessments of pre-analytics. RESULTS: Validated immunoassay and mass spectrometry-based assays were performed in CSF with a shift to plasma, a more accessible biofluid, as qualified assays became available. Performance comparisons across different CSF and plasma AD biomarker measurement platforms have enriched substantially the ADNI participant database enabling method performance determinations for AD pathology detection and longitudinal assessments of disease progression. DISCUSSION: Close collaboration with academic and industrial partners in the validation and implementation of AD biomarkers for early detection of disease pathology in treatment trials and ultimately in clinical practice is a key factor for the success of the work done in the Biomarker Core. HIGHLIGHTS: Describe ADNI Biomarker Core biobanking and sample distribution from 2007 to 2024. Discuss validated mass spectrometry and immunoassay methods for ADNI biofluid analyses. Review collaborations with academic and industrial partners to detect AD and progression. Discuss major challenges, and progress to date, for co-pathology detection. Implementation in the ATN scheme: co-pathology and modeling disease progression.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".