Speech and language impairments in behavioral variant frontotemporal dementia: A systematic review
Bibliographic record
Abstract
Abstract Although behavioral variant frontotemporal dementia (bvFTD) is classically defined by behavioral and socio-emotional changes, impairments often extend to other cognitive functions. These include early speech and language deficits related to the disease’s core neural disruptions. Yet, their scope and clinical relevance remains poorly understood. This systematic review characterizes such disturbances in bvFTD, considering clinically, neuroanatomically, genetically, and neuropathologically defined subgroups. We included 181 experimental studies, with at least 5 bvFTD patients diagnosed using accepted criteria, comparing speech and language outcomes between bvFTD patients and healthy controls or between bvFTD subgroups. Results reveal extensive and heterogeneous deficits across cohorts, with (a) consistent lexico-semantic, orthographic, and prosodic impairments; (b) inconsistent deficits in motor speech and grammar; and (c) relative preservation of phonological skills. Also, preliminary findings suggest that the severity of speech and language deficits might be associated with global cognitive impairment, predominantly temporal or fronto-temporal atrophy and MAPT mutations (vs C9orf72 ). Although under-recognized, these impairments contribute to patient characterization and phenotyping, while potentially informing diagnosis and prognosis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".