Differentiating sporadic frontotemporal dementia from late-onset primary psychiatric disorders
Bibliographic record
Abstract
Abstract Sporadic behavioural variant frontotemporal dementia (bvFTD) is often misdiagnosed as late-onset primary psychiatric disorder (PPD) due to overlapping symptoms and lack of biomarkers. We aimed to identify clinical features that distinguish sporadic bvFTD from PPD. Multi-centre baseline data were retrospectively retrieved and categorized into neuropsychological domains. Logistic regression models and receiver operating characteristic curves were conducted to determine discriminators. Data from 508 sporadic bvFTD and 152 PPD cases were included. Higher scores in cognitive screening [odds ratio (OR): 1.23], facial emotion processing (OR: 1.69), episodic memory (OR: 1.09), animal fluency (OR: 1.17), working memory (OR: 1.18), letter fluency (OR: 1.17) and depressive symptoms (OR: 7.41) were significantly associated with PPD (all Ps ≤ 0.010). Within a combined model, higher scores of letter fluency (OR: 1.47), cognitive screening (OR: 1.72) and lower attention (OR: 0.77) were significantly (all Ps ≤ 0.05) associated with PPD (area under the curve = 0.771). Neuropsychological measurements—letter fluency, cognitive screening and attention—can help distinguish sporadic bvFTD from late-onset PPD. Depressive symptoms and facial emotion processing emerged as potential discriminators, warranting further exploration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".