Neuropsychiatric symptom clusters targeted for treatment at earlier versus later stages of dementia
Bibliographic record
Abstract
OBJECTIVE: To characterize clusters of neuropsychiatric symptoms targeted for tracking the disease course in people with dementia, in relation to stage. METHODS: Baseline symptoms from 2922 subjects from two datasets (one clinic based, one online) were aggregated. Common neuropsychiatric symptoms identified by patients/carers as targets of treatment using a dementia SymptomGuide™ were selected. The Global Deterioration Scale was used for clinic staging, and an artificial neural network algorithm, for staging online subjects. Symptom clusters were detected using multiple correspondence analysis and connectivity graph analysis based on relative risk (RR). In a connectivity graph, each pair of nodes (representing symptoms) is connected if their co-occurrence is statistically significant; direction is indicated as positive if RR > 1 and negative otherwise. RESULTS: Neuropsychiatric symptoms were targeted for treatment in 1072 patients (37%). Agitation (37%) and sleep disturbances (28%) were most common symptoms. One cluster (in people with cognitive impairment, no dementia (CIND) or mild dementia) showed significant co-occurrence of anxiety and restlessness; decreased initiative was chiefly seen in isolation. A second cluster (in moderate/severe dementia) was defined by significant co-occurrence of delusions and hallucinations with sleep disturbances; in these subjects, decreased initiative was related to aggression. CONCLUSIONS: Two analytical methods identified neuropsychiatric symptom clusters targeted to track the disease course. In CIND/mild dementia, a profile of decreased initiative distinct from depression suggests possible executive dysfunction. In moderate/severe dementia, targets more reflected psychotic symptoms. Visual data displays allow the relationships between multiple symptoms to be considered simultaneously, which commonly is how they present in patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".