P2‐112: Effects of citalopram on neuropsychiatric symptoms in Alzheimer's dementia: Evidence from the citad study
Bibliographic record
Abstract
Citalopram has been associated with improvement in agitation in patients with Alzheimer's disease. The objective of the present study was to evaluate whether other neuropsychiatric symptoms improved with citalopram treatment compared to placebo. In this planned secondary analysis of the Citalopram for Agitation in Alzheimer Disease (CitAD) study, we evaluated the effect of citalopram on the 12 neuropsychiatric symptom domains assessed on the Neuropsychiatric Inventory. We compared changes between baseline and week 9 on citalopram 30 mg/day vs. placebo with regard to the presence of individual neuropsychiatric symptoms and individual domain scores in participants with symptoms at baseline. At week 9, participants treated with citalopram were less likely to report delusions (OR=0.40, p=0.03), anxiety (OR=0.43, p=0.01), and irritability/lability (OR=0.38, p=0.01). When comparing median scores for participants with symptoms present at baseline, differences favoring citalopram were seen for hallucinations (p<0.01) and favoring placebo for sleep/nighttime behavior (p=0.03). While dose constraints must be considered due to its side effect profile, citalopram's overall therapeutic effects in patients with Alzheimer's disease and agitation include reduction in the frequency of irritability, anxiety, and delusions; reduction in the severity of hallucinations, but an increase in the severity of sleep/nighttime behavior.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".