Adding the Topographical Information from Tau-PET to the A/T/(N) Framework: Steps Towards Staging AD in Vivo
Bibliographic record
Abstract
Biomarkers have revolutionized the study and clinical diagnosis of Alzheimer's disease (AD). While amyloid-β accumulation begins decades before the onset of clinical dementia in AD, tau pathology is more closely associated in both space and time to neurodegeneration and to clinical dysfunction. Correspondingly, tau-PET may prove useful in determining the severity of AD. Building on the biological research framework for AD, we review here methods and rationale to stage the severity of AD in vivo using the topographical distribution of tau-PET. We discuss how tau-PET can be used to detect early and subthreshold tau accumulation in medial temporal cortices prior to the onset of cognitive symptoms. Furthermore, tau-PET can be used to monitor the severity of AD as tau-PET spreads to association cortices and finally primary sensory cortices. We discuss the utility of tau-PET to monitor the progression of AD, the flexibility of potential approaches, and applications for clinical trials. In this regard, topographical information from tau-PET is a useful addition to the A/T/(N) framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".