Psychosis and dementia: risk factor, prodrome, or cause?
Bibliographic record
Abstract
BACKGROUND: Progression of dementia is often associated with the emergence of neuropsychiatric symptoms (NPS), though there is recent evidence that NPS may occur in prodromal dementia (PrD) and impact clinical course. Mood and anxiety symptoms are the NPS that tend to occur most frequently in PrD and thus have been most extensively studied. Comparatively, there has been little focus on psychotic symptoms in PrD. METHODS: The authors review the existing literature on psychosis in PrD, including the functional psychosis of early and late onset, with a focus on epidemiology, phenomenology, and clinical course and treatment considerations. RESULTS: Patients with psychotic disorders at baseline such as schizophrenia may be more at risk for developing dementia over time, although this is not completely clear. Psychotic symptoms are likely more common in PrD than previously understood based on factor analysis studies, although they are much more common in established dementia. Variability in findings may reflect the heterogeneous nature of PrD studies to date and the lack of inclusion of patients with late onset psychosis in most clinical studies. The presence of psychosis in patients with PrD may be associated with a worse prognosis in terms of mortality and conversion to dementia. CONCLUSIONS: Research to date suggests that psychosis in PrD may be more common than previously thought and impact clinical course negatively. Future studies incorporating patients with late onset psychotic disorders, and focusing on the impact of early recognition and treatment, are required to more fully understand the role of psychosis in PrD.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".