Evidence-based Interpretation of Amyloid-β PET Results
Bibliographic record
Abstract
BACKGROUND: Amyloid-β positron emission tomography (PET) allows for in vivo detection of fibrillar amyloid plaques, a pathologic hallmark of Alzheimer's disease (AD). However, amyloid-β PET interpretation is limited by the imperfect correlation between PET and autopsy, and the fact that it is positive in about 20% to 30% of cognitively normal individuals and non-AD dementias, especially when older or carrying the ε4 allele of apolipoprotein E (ApoE4). When facing a positive amyloid PET, clinicians have to evaluate the probability of a pathologic false positive as well as the probability of amyloid positivity being age-related, comorbid to a primary non-AD dementia (clinicopathologic false positive). These probabilities can be calculated to reach an evidence-based interpretation of amyloid-β. As literature review and calculations cannot be easily performed in the day-to-day clinic, we propose a clinician friendly, evidence-based Bayesian approach to the interpretation of amyloid-β PET results in the differential diagnosis of patients with cognitive impairment. METHODS: We defined AD as a clinicopathologic entity in which amyloid-β is the primary cause of cognitive impairment. We systematically reviewed the literature to estimate the sensitivity and specificity of amyloid-β PET against neuropathologic examination. We inferred rates of clinicopathologic false positivity (non-AD dementia with comorbid amyloid) based on age-dependent and ApoE-dependent prevalence of amyloid positivity in normal individuals and AD patients provided in large meta-analyses published by the Amyloid Biomarker Study Group. We calculated positive predictive value (PPV) and negative predictive value (NPV) of amyloid-β PET, which are presented in a clinician-friendly table. RESULTS: PPV of PET is highest in young ApoE4- patients with high pre-PET probability of AD. In older ApoE4+ patients with low pre-PET probability of AD, positive amyloid-β PET scans must be interpreted with caution. A negative amyloid-β PET makes a diagnosis of AD unlikely except in old patients with high pre-PET probability of AD. CONCLUSION: This evidence-based approach might provide guidance to clinicians and nuclear medicine physicians to interpret amyloid-β PET results for early and differential diagnosis of patients with progressive cognitive impairment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".