How Does Dementia Begin to Manifest in Bipolar Disorder? A Description of Prodromal Clinical and Cognitive Changes
Bibliographic record
Abstract
BACKGROUND: Older adults with bipolar disorder (BD) have increased dementia risk, but signs of dementia are difficult to detect in the context of pre-existing deficits inherent to BD. OBJECTIVE: To identify the emergence of indicators of early dementia in BD. METHODS: One hundred and fifty-nine non-demented adults with BD from the National Alzheimer's Coordinating Center (NACC) data repository underwent annual neuropsychological assessment up to 14 years (54.0 months average follow-up). Cognitive performance was examined longitudinally with linear mixed-effects models, and yearly differences between incident dementia cases and controls were examined in the six years prior to diagnosis. RESULTS: Forty participants (25.2%) developed dementia over the follow-up period ('incident dementia cases'). Alzheimer's disease was the most common presumed etiology, though this was likely a result of sampling biases within NACC. Incident dementia cases showed declining trajectories in memory, language, and speeded attention two years prior to dementia onset. CONCLUSION: In a sample of BD patients enriched for Alzheimer's type dementia, prodromal dementia in BD can be detected up to two years before onset using the same cognitive tests used in psychiatrically-healthy older adults (i.e., measures of verbal recall and fluency). Cognition in the natural course of BD is generally stable, and impairment or marked decline on measures of verbal episodic memory or semantic retrieval may indicate an early neurodegenerative process.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".