O1‐11‐03: Prevalence of Amyloid‐b Pathology in Primary Progressive Aphasia Variants: A Multicenter Study
Bibliographic record
Abstract
Primary progressive aphasia (PPA) is a clinical syndrome characterized by progressive loss of language function in the setting of focal degeneration in the language-dominant hemisphere. Since the 2011 Gorno-Tempini criteria, PPA is classified into logopenic (lvPPA), non-fluent (nfvPPA) and semantic (svPPA) variants. NfvPPA and svPPA are generally considered part of the frontotemporal dementia (FTD) spectrum, whereas lvPPA is frequently referred to as an atypical variant of Alzheimer’s disease (AD). Yet, no accurate prevalence estimates of amyloid-β pathology in these PPA variants are available. Therefore, this study aims to estimate the prevalence of PET/CSF biomarkers or neuropathological defined amyloid-positivity in PPA variants. We conducted an individual participant data meta-analysis using records of 607 PPA cases (204 lvPPA, 173 nfvPPA, 190 svPPA, 26 unclassified and 12 mixed) patients from 19 study sites (Table 1). Amyloid-positivity was defined using center-specific methods for neuropathology, CSF or PET analyses. The estimated prevalence of amyloid-positivity according to PPA variant, age and apolipoprotein E (APOE) ε4 status was determined using generalized estimating equation models. The mean prevalence of amyloid-β pathology in this meta-analysis was 80% for lvPPA, 20% for nfvPPA and 18% for svPPA. The prevalence of amyloid-β pathology increased with age in nfvPPA (p<0.001) and svPPA (p<0.01), but not in lvPPA (p=0.39, Figure 1). APOE ε4 carriers showed greater amyloid-positivity than non-carriers in all PPA variants (p<0.01, Figure 2).
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".