The Rationale and Design of Behavioral Interventions for Management of Agitation in Dementia in a Multi-Site Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Agitation and aggression are common in patients with Alzheimer's disease and related dementias and pose a significant burden on patients, caregivers, and the healthcare systems. Guidelines recommend personalized behavioral interventions as the first-line treatment; however, these interventions are often underutilized. The Standardizing Care for Neuropsychiatric Symptoms and Quality of Life in Dementia (StaN) study (ClinicalTrials.gov Identifier # NCT0367220) is a multisite randomized controlled trial comparing an Integrated Care Pathway, that includes a sequential pharmacological algorithm and structured behavioral interventions, with treatment-as-usual to treat agitation in dementia in long-term care and inpatient settings. OBJECTIVE: To describe the rationale and design of structured behavioral interventions in the StaN study. METHODS: Structured behavioral interventions are designed and implemented based on the following considerations: 1) personalization, 2) evidence base, 3) dose and duration, 4) measurement-based care, and 5) environmental factors and feasibility. RESULTS: The process to design behavioral interventions for each individual starts with a comprehensive assessment, followed by personalized, evidence-based interventions delivered in a standardized manner with ongoing monitoring of global clinical status. Measurement-based care is used to tailor the interventions and integrate them with pharmacotherapy. CONCLUSION: Individualized behavioral interventions in patients with dementia may be challenging to design and implement. Here we describe a process to design and implement individualized and structured behavioral interventions in the context of a multisite trial in long-term care and inpatient settings. This process can inform the design of behavioral interventions in future trials and in clinical settings for the treatment of agitation in dementia.
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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 | 0.332 | 0.302 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".