The Prevalence of Depressive Symptoms in Frontotemporal Dementia: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Depression is common in Alzheimer's and vascular dementia and is associated with poorer outcomes; however, less is known about the impact of depression on frontotemporal dementia (FTD). Here, we conducted a meta-analysis of diagnostic methods and the prevalence of depressive symptoms in FTD. METHODS: PubMed, EMBASE and PsychINFO were queried for 'depression' and/or 'depressive mood' in behavioral- and language-variant FTD. The prevalence and diagnosis of depressive symptoms were extracted from relevant studies and the results pooled using a random-effects model. RESULTS: We included 29 studies in this meta-analysis, with sample sizes ranging from 3 to 73 (n = 870). The omnibus estimated event rate of depressed mood was 0.334 (33%; 95% CI: 0.268-0.407). Symptoms were most commonly assessed via standardized neuropsychiatric rating scales, with other methods including subjective caregiver reports and chart reviews. The study results were heterogeneous due to the variability in diagnostic methods. CONCLUSIONS: Depressive symptoms similar to those in other dementias are commonly detected in FTD. However, the diagnostic methods are heterogeneous, and symptoms of depression often overlap with manifestations of FTD. Having a standardized diagnostic approach to depression in FTD will greatly facilitate future research in this area.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.034 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".