Clinical Correlates of the PET-based Braak Staging Framework in Alzheimer's Disease
Bibliographic record
Abstract
In vivo Alzheimer's disease diagnosis and staging is traditionally based on clinical features. However, the agreement between clinical and pathological Alzheimer's disease diagnosis, whose diagnosis assessment includes amyloid and Braak histopathological tau staging, is not completely convergent. The development of positron emission tomography (PET) tracers targeting neurofibrillary tangles offers prospects for advancing the staging of Alzheimer's disease from both biological and clinical perspectives. Recent advances in radiochemistry made it possible to apply the postmortem Braak staging framework to tau-PET images obtained in vivo. Here, our aim is to provide a narrative review of the current literature on the relationship between Alzheimer's disease clinical features and the PET-based Braak staging framework. Overall, the available studies support the stepwise increase in disease severity following the advance of PET-based Braak stages, with later stages being associated with worse cognitive and clinical symptoms. In line with this, there is a trend for unimpaired cognition, mild cognitive impairment, and Alzheimer's disease dementia to be compatible with early, intermediate, and late patterns of tau deposition based on PET-based Braak stages. Moreover, neuropsychiatric symptom severity seems to be linked to the extent of tau-PET signal across Braak areas. In sum, this framework seems to correspond well with the clinical progression of Alzheimer's disease, which is an indication of its potential utility in research and clinical practice, especially for detecting preclinical tau levels in individuals without symptoms. However, further research is needed to improve the generalizability of these findings and to better understand the applications of this staging framework.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".