Bipolar disorder and frontotemporal dementia: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To detail the biological, clinical and neurocognitive characteristics differentiating bipolar disorder (BD) from frontotemporal dementia (FTD) and to investigate whether BD is a risk factor for FTD. METHODS: A total of 16 studies were included in this systematic review. Five studies described biological and/or neurocognitive characteristics between patients with BD and FTD, and 11 studies investigated whether BD was a risk factor for FTD. RESULTS: Individuals with FTD presented higher levels of serum neurofilament light chain, greater grey matter reduction in frontal, parietal and temporal lobes, and increased slow wave oscillations in channels F3, F4, T3, T5, T4 and T6 within an electroencephalogram (EEG), relative to individuals with BD. Patients with FTD presented greater deficits in executive function and theory of mind compared to patients with BD in a euthymic state, and more deficits in verbal fluency compared to patients with BD in a current mood episode. Patients with BD in a current mood episode showed greater impairment in attention, working memory, verbal memory and executive function relative to individuals with FTD. In addition, retrospective studies showed that 10.2%-11.6% of patients with behavioural variant FTD (bvFTD) had a preceding history of BD. CONCLUSION: Biological and neurocognitive characteristics help to distinguish between BD and FTD, and it may help to reach a more precise diagnosis. In addition, individuals with BD are at higher risk of developing FTD. More studies are needed to identify the predictors of the conversion between BD to FTD.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".