A longitudinal study of language decline in Alzheimer's disease and frontotemporal dementia
Bibliographic record
Abstract
Language decline is usually the fastest and predominant change in primary progressive aphasia (PPA). In Alzheimer's disease (AD), it is usually associated with global cognitive deficits. Decreased speech output, reduced conversational initiation, echolalia, and changes in the pragmatics of conversation are seen in the behavioral variant of frontotemporal dementia (FTD-bv), however, the evolution of language disturbance in FTD-bv patients is rarely examined systematically with a standardized language battery. We aimed to longitudinally track the nature of language change in FTD-bv, PPA, and AD using a standardized measure of language functioning. We also explored the nature of language deficits between semantic dementia (SD) patients and the fluent subgroup of PPA patients. The Western Aphasia Battery was administered to 105 AD, 20 FTD-bv, 54 PPA, and 10 SD patients on 2 occasions with approximately 1 year between assessments. Ninety-nine of these patients were examined an additional year. FTD-bv and PPA patients showed a faster language decline than AD patients. The eventual overlap in language functioning in FTD-bv and PPA suggests that these syndromes belong to the same spectrum of disorders. In conclusion, longitudinal language assessment provides us with a unique understanding of the evolution and progression of language deterioration in various dementias.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".