Language Profile of Posterior Cortical Atrophy: A Comparative Study with Alzheimer’s Disease Variants
Bibliographic record
Abstract
Abstract Objective This study investigates language impairments in early-stage posterior cortical atrophy (PCA) patients, examining five language subdomains to resolve existing controversies and gaps in the literature. Methods Participants diagnosed with posterior cortical atrophy (PCA; n=105), typical Alzheimer’s disease (tAD; n=105), logopenic variant primary progressive aphasia (lvPPA; n=116) and healthy controls (HC; n=165) were selected from the National Alzheimer’s Coordinating Center (NACC) database. We utilized language tests from the Uniform Data Set and Frontotemporal Lobar Degeneration Module to assess different aspects of linguistic ability, including verbal fluency, reading, naming, semantics and repetition. Result Our findings revealed a global decline in visual and non-visual language functions among PCA patients compared to HC, with no spared domains. Furthermore, we investigated specific language errors in reading and sentence repetition, and we found that PCA patients committed a mix of phonological, semantic and word omission errors. They were more impaired on irregular vs. regular word reading and more impaired on verb vs noun naming. Overall PCA patients showed less severe language deficits than lvPPA, except in single word comprehension and verb naming, where the opposite pattern was found. They also showed more impaired visual language impairments and similar non-visual language impairments in comparison to tAD. Discussion These findings highlight that language impairments in PCA extend beyond visual deficits, playing a key role in its clinical presentation. Recognizing these language issues is essential for differentiating PCA from tAD and lvPPA, where distinct patterns of impairment help refine diagnosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".