Pre-psychosis in later life as a risk factor for progressive cognitive decline: Findings from the IPA psychosis in neurodegenerative disease working group
Bibliographic record
Abstract
Pre-clinical Alzheimer's disease (AD) has traditionally been characterized by subtle cognitive deficits alongside biomarker changes. However, emerging evidence suggests a spectrum of neuropsychiatric changes, including apathy, affective disturbances, agitation, impulse control deficits, and psychosis, may precede cognitive decline. Late-onset psychotic disorders, such as Very Late-Onset Schizophrenia-Like Psychosis (VLOSLP), differ from pre-psychosis, the latter presenting with subtle symptoms and retained insight. These subtler late-life onset symptoms are associated with incident cognitive decline, particularly in APOE4 carriers. Screening with tools such as the Mild Behavioral Impairment Checklist (MBI-C) enables the standardisation of measurement, facilitating identification of at-risk individuals. Plasma biomarkers and neuropsychological assessments further aid diagnosis and risk stratification. Understanding the link between pre-psychosis and dementia-related psychosis will be crucial, as AD with psychosis is associated with a more aggressive disease course. Identifying and treating these individuals early may improve clinical outcomes and facilitate timely intervention with disease-modifying therapies. Moreover, there remains a need to better define in what circumstances treatment interventions are indicated and what those interventions should be.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".