F4‐02‐02: THE S‐CITAD STUDY: TRIAL DESIGN AND METHODS FOR THE S‐CITALOPRAM IN AGITATION TRIAL
Bibliographic record
Abstract
Agitation is common in Alzheimer's disease (AD) and a major burden to patients and caregivers. Effective therapies for agitation are needed, combining non-pharmacologic with novel pharmacologic approaches and targeting therapies to appropriate subgroups of this heterogeneous condition. Targeting the serotonin system, we conducted the Citalopram in AD (CitAD) Study, a randomized, double blind, placebo-controlled trial that administered a psychosocial intervention to all randomized participants to the SSRI citalopram at 30mg/day or placebo. In CitAD, racemic citalopram was effective for agitation with 40% of citalopram-treated participants experiencing substantial clinical improvement vs. only 26% on placebo. However, citalopram was associated with cognitive worsening and prolongation of the ECG-QTc interval. In blood concentration models, cognitive and cardiac changes were associated with the R-enantiomer, whereas clinical improvements were primarily associated with the S-enantiomer (escitalopram). We proposed the S-CitAD Study to build on and extend these findings, applying an evidence-based, sequential approach to treating agitation in AD with real world applicability for patients. In this Phase 3 study, we will enroll 589 patients with AD and agitation and employ an adaptive design with a psychosocial intervention administered at the enrollment visit. Patients not showing clinically significant improvement in agitation after 3 weeks will be randomized to drug or placebo. Given the findings of CitAD, we are using escitalopram at a target dose of 15 mg/day instead of the racemic citalopram. We will evaluate the efficacy and safety of escitalopram over 12-weeks (AIM 1). We will also evaluate pre-specified predictors of response suggested by CitAD (AIM 2). Finally, we will follow patients showing significant improvement on the psychosocial intervention to study predictors of response, as well as duration of improvement and frequency, time course, and predictors of subsequent relapse (AIM 3). N/A.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".