Electroconvulsive Therapy with a Memory Reactivation Intervention for Post-Traumatic Stress Disorder: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Introduction Post-traumatic Stress Disorder (PTSD) often does not respond to available treatments. Memories are vulnerable to disruption during reconsolidation, and electroconvulsive therapy (ECT) has amnestic effects. We sought to exploit this phenomenon as a potential treatment for PTSD with a clinical trial of patients with PTSD receiving ECT. Methods Twenty-eight participants with severe depression with comorbid PTSD referred for ECT treatment were randomly assigned to reactivation of a traumatic or non-traumatic memory using script driven imagery prior to each ECT treatment. Primary outcomes were change in scores on the Modified PTSD Symptom Scale - Self Report (MPSS-SR) and the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Assessments were completed by blinded raters. Secondary outcomes included a comparison of the change in heart rate while listening to the script. Results Twenty-five patients who completed a post-ECT assessment were included in the analysis. No significant group differences were found in the MPSS-SR or CAPS-5 scores from pre-ECT to post-ECT or 3-month follow-ups. However, both groups improved at post-ECT and 3-month follow up. Partial eta squared estimates of effect size showed large effect sizes for all outcomes (η 2 > 0.13). Changes in heart rate were not significantly different between groups or over time. Conclusions In this RCT, ECT paired with pre-treatment traumatic memory reactivation was not more effective for treating PTSD symptoms than ECT alone. While our primary hypothesis was not supported, our data provides further support for the efficacy of ECT for improving symptoms of PTSD with comorbid depression. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04027452 Identifier: NCT04027452
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".