Citalopram for agitation in Alzheimer's disease: Design and methods
Bibliographic record
Abstract
BACKGROUND: Agitation is one of the most common neuropsychiatric symptoms of Alzheimer's disease (AD), and is associated with serious adverse consequences for patients and caregivers. Evidence-supported treatment options for agitation are limited. The citalopram for agitation in Alzheimer's disease (CitAD) study was designed to evaluate the potential of citalopram to ameliorate these symptoms. METHODS: CitAD is a randomized, double-masked, placebo-controlled multicenter clinical trial, with two parallel treatment groups assigned in a 1:1 ratio and randomization stratified by clinical center. The study included eight recruiting clinical centers, a chair's office, and a coordinating center located in university settings in the United States and Canada. A total of 200 individuals having probable AD with clinically significant agitation and without major depression were recruited for this study. Patients were randomized to receive citalopram (target dose of 30 mg/d) or matching placebo. Caregivers of patients in both treatment groups received a structured psychosocial therapy. Agitation was compared between treatment groups using the NeuroBehavioral Rating Scale and the AD Cooperative Study- Clinical Global Impression of Change, which are the primary outcomes. Functional performance, cognition, caregiver distress, and rates of adverse and serious adverse events were also measured. CONCLUSION: The authors believe the design elements in CitAD are important features to be included in trials assessing the safety and efficacy of psychotropic medications for clinically significant agitation in AD.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".