Psychosocial or clinico‐demographic factors related to neuropsychiatric symptoms in patients with Alzheimer's disease needing interventional treatment: analysis of the CATIE‐AD study
Bibliographic record
Abstract
OBJECTIVE: This study sought to determine psychosocial and clinico-demographic factors related to each symptomatic cluster (i.e., aggressiveness, psychosis, apathy/eating problems, and emotion/disinhibition) of neuropsychiatric symptoms (NPSs) in patients with Alzheimer's disease (AD) needing interventional treatment against their agitation or psychotic symptoms. These clusters were classified from 12 Neuropsychiatric Inventory (NPI) subscores in our previous study using the Clinical Antipsychotic Trials of Intervention Effectiveness-Alzheimer's Disease (CATIE-AD) dataset. METHODS: Based on clinical data from 421 AD outpatients with agitation or psychotic symptoms needed interventional treatment enrolled in the CATIE-AD, we conducted logistic regression analyses to examine the relationships between each symptomatic cluster and three psychosocial (marital status, residence, and caregivers' burden) and nine clinico-demographic (age, gender, education year, general cognition, activity of daily living [ADL], general medical health, race, and intake of anti-dementia drugs or psychotropics) factors. RESULTS: While no factor contributed to aggressiveness, psychosis was associated with several clinico-demographic factors: female gender, non-Caucasian race, and lower cognitive function. Apathy/eating problems was associated with more severe caregiver burden, living in one's own home, lower ADL level, and male gender, while emotion/disinhibition was predicted by more severe caregiver burden, lower education level, not-married status, and younger age. CONCLUSIONS: Among the four NPS clusters, apathy/eating problems and emotion/disinhibition were associated with psychosocial as well as clinico-demographic factors in AD patients with psychotic symptoms or agitation needed interventional treatment. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".