Efficacy and safety of maintenance electroconvulsive therapy for sustaining resolution of severe aggression in a major neurocognitive disorder
Bibliographic record
Abstract
We report a novel electroconvulsive therapy (ECT) regimen for sustaining the resolution of behavioural and psychological symptoms of dementia (BPSD) using alternating acute and maintenance ECT (M-ECT) trials. A 64-year-old man presenting with major neurocognitive disorder was admitted for acute behavioural disturbances and physical aggression. With few treatment options, the impact on patients' quality of life often supersedes cognitive symptoms and is a predictor of long-term institutionalisation. Recent studies indicate that ECT may be an effective and safe way to address BPSD. Clinicians have little information about when and how to stop a successful course of acute ECT or the long-term advantages of M-ECT with subsequent intermittent acute ECT. This case emphasises the benefit of M-ECT and describes potential challenges associated with abrupt discontinuation. This case is the first to detail tapering ECT for treatment of aggression in dementia by interchanging acute and M-ECT courses in response to symptom burden.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".