Citalopram for the Treatment of Agitation in Alzheimer Dementia
Bibliographic record
Abstract
OBJECTIVE: To assess potential genetic influences on citalopram treatment efficacy for agitation in individuals with Alzheimer dementia (AD). Six functional genetic variants were studied in the following genes: serotonin receptor 2A (HTR2A-T102C), serotonin receptor 2C (HTR2C-Cys23Ser), serotonin transporter (5HTT-LPR), brain-derived neurotropic factor (BDNF-Val66Met), apolipoprotein E (ε2, ε3, ε4 variants), and cytochrome P450 (CYP2C19). Treatment response by genotype was measured by (1) the agitation domain of the Neurobehavioral Rating Scale, (2) the modified Alzheimer Disease Cooperative Study-Clinical Global Impression of Change scale (mADCS-CGIC), (3) the agitation domain of the Neuropsychiatric Inventory (NPI), and (4) the Cohen-Mansfield Agitation Inventory. METHOD: We utilized data from the Citalopram for Agitation in Alzheimer's Disease (CitAD) database. CitAD was a 9-week randomized, double-blind, placebo-controlled multicenter clinical trial showing significant improvement in agitation and caregiver distress in patients treated with citalopram. Proportional odds logistic regression and mixed effects models were used to examine the above-mentioned outcome measures. RESULTS: Significant interactions were noted on the NPI agitation domain for HTR2A (likelihood ratio [LR] = 6.19, df = 2, P = .04) and the mADCS-CGIC for HTR2C (LR = 4.33, df = 2, P = .02) over 9 weeks. DISCUSSION: Treatment outcomes in CitAD showed modest, although statistically significant, influence of genetic variation at HTR2A and HTR2C loci. Future studies should continue to examine the interaction of known genetic variants with antidepressant treatment in patients with AD having agitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".