Cognitive-Behavioral Treatment for Anxiety in Patients With Dementia: Two Case Studies
Bibliographic record
Abstract
Anxiety is common in dementia and is associated with decreased independence and increased risk of nursing home placement. However, little is known about the treatment of anxiety in dementia. This article reports results from two patients who were treated with a modified version of cognitive-behavioral therapy for anxiety in dementia (CBT-AD). Modifications were made in the content, structure, and learning strategies of CBT to adapt skills to the cognitive limitations of these patients and include collaterals (i.e., family members, friends, or other caregivers) in the treatment process. The patients received education and awareness training and were taught the skills of diaphragmatic breathing, coping self-statements, exposure, and behavioral activation. The Clinical Dementia Rating (CDR) Scale was used to characterize dementia severity and determine eligibility for treatment (a CDR score of 0.5 to 2.0 was required for participation). Other measures included the Rating Anxiety in Dementia scale, the Neuropsychiatric Inventory Anxiety subscale, and the Mini International Neuropsychiatric Interview. Outcome data showed improvement in anxiety as measured by standardized rating scales. We conclude that CBT-AD is potentially useful in treating anxiety in dementia patients and that this technique merits further study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".