What Is Semantic Dementia?
Bibliographic record
Abstract
OBJECTIVES: To describe a large, clinically defined cohort of patients with semantic dementia (SD) that highlights important, sometimes overlooked features and to compare it with similar entities. DESIGN: Cohort study. SETTING: A cognitive neurology clinic. PATIENTS: A population of 48 patients clinically diagnosed with SD was contrasted with 52 patients with progressive nonfluent aphasia, 42 patients with a behavioral variety of frontotemporal dementia, and 105 patients with Alzheimer disease on speech output characteristics, comprehension, naming, and repetition subtests of the Western Aphasia Battery, the Frontal Behavioral Inventory, and other cognitive tests. Neuroimaging was visually analyzed, and 6 patients with SD had autopsy. RESULTS: Of 37 patients with probable SD, 48.6% had semantic jargon; 21.6%, excessive garrulous output; and 75.7%, some pragmatic disturbance. Semantic substitutions were frequent in SD (54.1%) but phonological errors were absent, in contrast to progressive nonfluent aphasia with the opposite pattern. All but 3 patients with probable SD questioned the meaning of words. Patients with SD had significantly lower naming and comprehension scores, and their fluency was between progressive nonfluent aphasia and Alzheimer disease or behavioral frontotemporal dementia. Behavior was abnormal in 94.6% of patients with probable SD. CONCLUSIONS: Semantic dementia is distinguishable from other presentations of frontotemporal dementia and Alzheimer disease, not only by fluent speech and impaired comprehension without loss of episodic memory, syntax, and phonology but also by empty, garrulous speech with thematic perseverations, semantic paraphasias, and poor category fluency. Questioning the meaning of words (eg, "What is steak?") is an important diagnostic clue not seen in other groups, and behavior change is prevalent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".