Escitalopram for agitation in Alzheimer's disease (S‐CitAD): Methods and design of an investigator‐initiated, randomized, controlled, multicenter clinical trial
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a disabling, common cause of dementia, and agitation is one of the most common and distressing symptoms for patients with AD. Escitalopram for agitation in Alzheimer's disease (S-CitAD) tests a novel, clinically derived therapeutic approach to treat agitation in patients with AD. METHODS: S-CitAD is a NIH-funded, investigator-initiated, randomized, multicenter clinical trial. Participants receive a structured psychosocial intervention (PSI) as standard of care. Participants without sufficient response to PSI are randomized to receive 15 mg escitalopram/day or a matching placebo in addition to PSI. Primary outcome is the Modified Alzheimer's Disease Cooperative Study - Clinical Global Impression of Change (mADCS-CGIC). DISCUSSION: S-CitAD will provide information about a practical, immediately available approach to treating agitation in patients with AD. S-CitAD may become a model of how to evaluate and predict treatment response in patients with AD and agitation as a neuropsychiatric symptom (ClinicalTrials.gov Identifier: NCT03108846).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".