Revisiting Criteria for Psychosis in Alzheimer’s Disease and Related Dementias: Toward Better Phenotypic Classification and Biomarker Research
Bibliographic record
Abstract
BACKGROUND: Psychotic symptoms are common in Alzheimer's disease (AD) and related neurodegenerative disorders and are associated with more rapid disease progression and increased mortality. It is unclear to what degree existing criteria are utilized in clinical research and practice. OBJECTIVE: To establish research criteria for the diagnosis of psychosis in AD. METHODS: The International Society to Advance Alzheimer's Research and Treatment (ISTAART) Neuropsychiatric Symptoms (NPS) Professional Interest Area (PIA) psychosis subgroup reviewed existing criteria for psychosis in AD and related dementias. Through a series of in person and on-line meetings, a priority checklist was devised to capture features necessary for current research and clinical needs. PubMed, Medline and other relevant databases were searched for relevant criteria. RESULTS: Consensus identified three sets of criteria suitable for review including those of Jeste and Finkel, Lyketsos, and the Diagnostic and Statistical Manual for Mental Disorders, 5th edition. It was concluded that existing criteria could be augmented by including a more specific differentiation between delusions and hallucinations, address overlap with related conditions (agitation in particular), adding the possibility of symptoms emerging in the preclinical and prodromal phases, and building on developing research in disease biomarkers. CONCLUSION: We propose criteria, developed to improve phenotypic classification of psychosis in AD, and advance the research agenda in the field to improve epidemiological, biomarker, and genetics research in the field. These criteria serve as a complement to the International Psychogeriatric Association criteria for psychosis in neurocognitive disorders.
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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 | 0.079 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".