The Treatment of Disruptive Vocalization in Dementia (Behavioral and Psychological Symptoms of Dementia) With Electroconvulsive Therapy
Bibliographic record
Abstract
OBJECTIVE: There is emerging evidence that electroconvulsive therapy (ECT) can help with the behavioral and psychological symptoms of dementia. One of the most distressing behavioral symptoms of dementia is disruptive vocalization. Previous small case series have suggested that antidepressants and ECT can be beneficial for this distressing condition. The aim of this study was to describe the successful use of ECT in treating 5 patients with disruptive vocalization. METHODS: A retrospective chart review of 5 patients with dementia of mixed etiologies was conducted comparing pretreatment and posttreatment scores on the Cohen-Mansfield Agitation Inventory. All 5 patients had unsuccessful treatments with nonpharmacological methods and pharmacotherapy including antidepressants. RESULTS: After completion of a series of ECT, the mean verbal agitation score decreased from 6.8 (95% confidence interval, 6.3-7.3) to 2.3 (95% confidence interval, 1.3-3.3), with both clinical and statistical significance (P < 0.001). CONCLUSIONS: Although further research is needed, these findings support considering the use of ECT for disruptive vocalization 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".