Neuropsychiatric and behavioral symptom clusters in frontotemporal dementia
Bibliographic record
Abstract
Background: Non-Alzheimer's disease dementias, including frontotemporal dementia (FTD) can be difficult to characterize due to the predominance of distinct behavioral and neuropsychiatric symptoms. Widely used measurement tools lack structure and objectivity. Objective: The purpose of this study was to use systematic direct observation of neuropsychiatric and behavioral symptoms, via the Neurobehavioral Rating Scale (NBRS), to characterize clusters of behavioral and neuropsychiatric symptoms in FTD and examine how selected symptom clusters correlate with structural neuroimaging. Methods: We performed a factor analysis on the NBRS data from 172 patients with FTD and examined the neural correlates of the selected symptom clusters in a subsample of 67 patients. Results: Six factors accounted for 56% of total variance across NBRS item scores: Apathy/Blunting, Agitation/Disinhibition, Cognitive/Language, Planning/Insight, Anxiety/Lability, and Psychosis. Symptom clusters showed significant associations with specific regions of cortical thinning: Agitation/Disinhibition with bilateral frontal regions, and Cognition/Language with the left bank of the superior temporal sulcus and supramarginal regions. Conclusions: The selected symptom clusters associated with known regions of atrophy in FTD. The NBRS is an effective observational measure that may extend characterization and understanding of FTD.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".