Biomarker modelling of Alzheimer's disease using in vivo Braak staging
Bibliographic record
Abstract
Abstract Background Gold standard diagnostic methods for AD rely on staging systems to measure disease severity, which have not yet been incorporated into the in vivo biological research framework for AD. The topographical information conferred by tau‐PET offers the potential to translate the gold standard histopathological Braak staging system to living individuals. Method Using the topographical information from [18F]MK6240 tau‐PET, we applied the Braak tau staging system to 324 living individuals. We used PET‐based Braak stage to model the trajectories of amyloid‐b, phosphorylated tau in cerebrospinal fluid (pTau181, pTau217, pTau231, pTau235) and plasma (pTau181, pTau231), neurodegeneration and cognitive symptoms. Result PET‐based Braak stages were We identified nonlinear AD biomarker trajectories corresponding to the spatial extent of tau‐PET, with modest biomarker changes detectable by Braak stage II and significant changes occurring at stages III‐IV, followed by plateaus. Early Braak stages were associated with isolated memory impairment, while Braak stages V‐VI were incompatible with normal cognition. Follow up tau‐PET scans indicated sequential progression of in vivo Braak stages over time, with progression beyond Braak stage III requiring the presence of amyloid‐beta abnormality. Conclusion Our results support PET‐based Braak staging as a framework to model the natural history of AD, as well as monitor AD severity from presymptomatic to clinical dementia phases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".