TRIAD multi‐dimensional biobank for biomarker discovery
Bibliographic record
Abstract
Abstract Background The Translational Biomarkers in Aging and Dementia (TRIAD) is a longitudinal, biomarker‐based cohort designed to study interactions between the pathophysiological processes driving to dementia. Currently, 50 million people worldwide have dementia [1]. PET and MRI imaging have been widely used to assess disease stage, though expensive and not readily accessible [2]. More recently though, breakthroughs in blood‐based biomarkers have proven a useful, available, and more cost‐effective way of evaluating amyloid and tau positivity. The TRIAD Biobank provides a unique framework to study disease stage using blood‐based biomarkers in conjunction with PET and CSF studies. Our cohort allows for validation of novel biomarkers as we simultaneously acquire gold standard second generation imaging acquisitions with full spectrum fluid biomarkers (CSF, saliva, plasma, urine). Importantly, the data acquired is integrated in a database compliant to multi‐dimensional biomarker analysis. Method Figure 1 depicts the cohort's banked fluids by diagnostic group. Figure 2 depicts the cohort's banked fluids by sex grouping. Result The TRIAD Biobank stores 1,034 blood samples from 655 participants, 658 of these being follow‐up collections. Additionally, cerebrospinal fluid has been collected from 381 individuals accounting for 520 collections in the bank, 812 urine collections from 662 individuals and 591 saliva samples from 432 individuals. Additionally, the cohort has collected 761 tau PET scans using [ 18 F]MK6240, 644 amyloid PET scans using [ 18 F]AZD4694 and 804 MRI using 3 Tesla. Conclusion With dementia being one of the leading, most costly causes of death, the need for early detection, more affordable, less invasive, and more readily available ways to determine disease stage is apparent [2]. The TRIAD multi‐dimensional biobank provides a wealth of resources to discover affordable biomarkers needed for early diagnosis and Alzheimer’s disease prevention. [1] Patterson, C. 2018. World Alzheimer Report 2018: The state of the art of dementia research, new frontiers . London, England: Alzheimer's Disease International [2] Gauthier S, Rosa‐Neto P, Morais JA, & Webster C. 2021. World Alzheimer Report 2021: Journey through the diagnosis of dementia . London, England: Alzheimer’s Disease International.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".