Language deficits in major forms of dementia and primary progressive aphasias: an update according to new diagnostic criteria
Bibliographic record
Abstract
In this review, we report current data on spoken and written language disorders in the most frequent dementia syndromes, namely Alzheimer' disease, vascular cognitive impairment and dementia with Lewy bodies. Language deficits are also the core features of three variants of primary progressive aphasia, namely the nonfluent/agrammatic, semantic and logopenic variants. This review reveals that, like other cognitive functions, language is highly vulnerable to neurodegenerative diseases. For some, language deficits result from impairment in linguistic processes per se, while for others, they are the direct consequence of impairments affecting working memory and executive functions. Language deficits in Alzheimer's disease and in nonfluent/agrammatic and semantic variants of primary progressive aphasia are well documented. By contrast, those about vascular cognitive impairment and dementia with Lewy bodies remain scarce and limited to large cognitive domains. The identification of logopenic variant of primary progressive aphasia is very recent, and more research is needed to complete the clinical description and identification of the functional origin of the disorders. Finally, knowledge on the impairment of written language in neurodegenerative diseases is less well documented than those on spoken language deficits. Other studies are therefore needed to improve the description of linguistic profiles and to provide additional elements to help in the differential 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".