Electroconvulsive therapy in patients with trauma and personality disorders: what is the evidence?
Bibliographic record
Abstract
INTRODUCTION: Electroconvulsive therapy (ECT) is one the most effective interventions for treatment-resistant depression. There is a link between trauma history and the development of depression, and frequent comorbidity with post-traumatic stress disorder (PTSD) and personality disorders. The impact of comorbid trauma and personality disorders on the efficacy of ECT has attracted research interest, with the possibility raised for using ECT to target other core symptoms of these disorders. AREAS COVERED: In this scoping review, the authors describe the available evidence on the use of ECT in personality disorders, PTSD, and individuals with a trauma history. The article is based on literature derived from Embase, APA PsycInfo, Web of Science, CINAHL, and Cochrane CENTRAL databases on relevant studies published up until 9 December 2024. EXPERT OPINION: Preliminary evidence supports the efficacy of ECT to treat depressive symptoms in patients with comorbid PTSD while there is more conflicting evidence for its use in patients with comorbid personality disorders, particularly borderline personality disorder. A careful exploration of trauma history, baseline mood symptoms, and previous treatment trials should be undertaken prior to the recommendation of ECT. Randomized controlled trials are needed to verify the therapeutic benefits of ECT for core symptom domains in other disorders.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".