Prevalence of Amyloid Pathology in Primary Progressive Aphasia Variants: A Systematic Review and Meta-Analysis (P2.235)
Bibliographic record
Abstract
Objective: Estimate the prevalence of amyloid pathology in variants of primary progressive aphasia (PPA) through a meta-analysis of published studies Background: 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 pathology in these PPA variants are available. Methods: The PubMed electronic database was searched for studies including neuropathological and/or biomarker (cerebrospinal fluid (CSF) analysis or amyloid imaging) measures of amyloid pathology in PPA subtypes. We adopted the center-specific thresholds for amyloid-positivity, and studies presenting duplicate patients or amyloid-positive cases only were excluded. We included 23 studies encompassing 569 PPA cases (275 lvPPA, 155 nfvPPA, 139 svPPA) in this meta-analysis. We are currently collecting individual-patient data from many centers worldwide to provide prevalence estimates of amyloid-positivity adjusted for age and ApoEe4 status. Results: When pooling data from all 23 studies, the estimated prevalence of amyloid pathology was 81[percnt] for lvPPA, 24[percnt] for nfvPPA and 14[percnt] for svPPA. Neuropathology studies consistently reported lower prevalence of amyloid pathology in lvPPA (59[percnt]) than studies using CSF (82[percnt]) or amyloid imaging (87[percnt]). Most neuropathology studies (6/8 studies, 106/165 patients) were published before the 2011 Gorno-Tempini criteria, when lvPPA was not yet well defined. Conclusions: This meta-analysis supports the notion that the majority of patients with an lvPPA phenotype harbor amyloid pathology, while semantic and non-fluent variants of PPA are most likely associated with non-AD pathology. This indicates that the proposed classification scheme for PPA helps predicting the underlying pathology.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".