PET Amyloid and Tau Status Are Differently Affected by Patient Features
Bibliographic record
Abstract
BACKGROUND: Amyloid-β (Aβ) plaques and tau neurofibrillary tangles are two neuropathological hallmarks of Alzheimer's disease (AD), which both can be visualized in vivo using PET radiotracers, opening new opportunities to study disease mechanisms. OBJECTIVE: Our study investigated 11 non-PET factors in 5 categories (including demographic, clinical, genetic, MRI, and cerebrospinal fluid (CSF) features) possibly affecting PET amyloid and tau status to explore the relationships between amyloid and tau pathology, and whether these features had a different association with amyloid and tau status. METHODS: We included 372 nondemented elderly from the Alzheimer's Disease Neuroimaging Initiative cohort. All underwent PET amyloid and tau analysis simultaneously, and were grouped into amyloid/tau quadrants based on previously established abnormality cut points. We examined the associations of above selected features with PET amyloid and tau status using a multivariable logistic regression model, then explored whether there was an obvious correlation between the significant features and PET amyloid or tau levels. RESULTS: Our results demonstrated that PET amyloid and tau status were differently affected by patient features, and CSF biomarker features provided most significant values associating PET findings. CSF Aβ42/40 was the most important factor affecting amyloid PET status, and negatively correlated with amyloid PET levels. CSF pTau could significantly influence both amyloid and tau PET status. Besides CSF pTau and Aβ42, APOEɛ4 allele status and Mini-Mental State Examination scores also could influence tau PET status, and significantly correlated with tau PET levels. CONCLUSION: Our results support that tau pathology possibly affected by Aβ-independent factors, implicating the importance of tau pathology in AD pathogenesis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".