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Record W2781782784 · doi:10.1136/bcr-2017-222100

Efficacy and safety of maintenance electroconvulsive therapy for sustaining resolution of severe aggression in a major neurocognitive disorder

2018· article· en· W2781782784 on OpenAlexaff
Melanie Isabella Selvadurai, R Waxman, Omar Ghaffar, Ilan Fischler

Bibliographic record

VenueBMJ Case Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsOntario Shores Centre for Mental Health SciencesMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsElectroconvulsive therapyNeurocognitiveAggressionDiscontinuationDementiaPsychiatryPsychologyQuality of life (healthcare)MedicineCognitionPsychotherapistInternal medicineDisease

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.327
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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