Electroconvulsive Therapy for Neuropsychiatric Symptoms due to Major Neurocognitive Disorder
Bibliographic record
Abstract
OBJECTIVES: Neuropsychiatric symptoms (NPSs) in those with major neurocognitive disorder (MNCD) include the responsive behaviors of agitation and aggression. Electroconvulsive therapy (ECT) has shown some effectiveness based on retrospective studies and one open label prospective study. We hypothesized that ECT will reduce NPSs between baseline and after treatment in those with medication-refractory behaviors. METHOD/DESIGN: This Canadian prospective multicenter study included MNCD patients admitted to geriatric psychiatry units for the management of refractory NPSs. All treatment-refractory participants suffered from advanced MNCD. We conducted the Neuropsychiatric Inventory-Clinician version and the Pittsburgh Agitation Scale at baseline, and during and after the ECT course. A bitemporal or bifrontal ECT series based on dose titration to 1.5 to 2.5 times seizure threshold was administered. RESULTS: Data were collected for 33 patients with a mean age of 73 and categorized with severe MNCD using the Functional Assessment Staging of Alzheimer's Disease scale (stages 6 and 7). The data showed a drop in mean Neuropsychiatric Inventory-Clinician version from 58.36 to 24.58 (P < 0.0001). Mean Neuropsychiatric Inventory agitation subscale dropped from 7.12 to 3.09 (P = 0.007). Mean Neuropsychiatric Inventory aggression subscale dropped from 6.94 to 0.97 (P < 0.0001). There was a concomitant significant decline in Pittsburgh Agitation Scale scores. No participants dropped out because of intolerance of ECT. One participant died from pneumonia, which did not appear related to ECT. CONCLUSIONS: In this naturalistic study, ECT was found to be a safe and effective treatment for certain NPSs in people with MNCD. This can translate into improving quality of life.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".