Improvements in Health-Related Quality of Life With Electroconvulsive Therapy
Bibliographic record
Abstract
INTRODUCTION: Although the antidepressant efficacy of ECT is well documented, patient-reported outcomes after this treatment are less well characterized. The aims of the current meta-analysis are to quantify the impact of an acute course of ECT on health-related quality of life (HRQoL) and to identify related moderators, specifically post-ECT depressive symptom remission and patient age. METHODS: We searched PubMed, PsycINFO, and Web of Science databases for randomized and nonrandomized studies that report on changes in HRQoL measures after an acute course of ECT. Only studies that used the Medical Outcomes Study Short Form 36 (SF-36) instrument were included. A random effects model using the Hedges' g effect size was used in calculating the pre-post ECT outcomes on all 8 SF-36 subscales and the SF-36 total scores including the physical and mental composite scores. Subgroup analyses were conducted using remission status and age as moderators. RESULTS: Four studies contributed to this analysis. Significant improvements across all subscales of the SF-36 were observed. Large and very large effect sizes were present for both the SF-36 physical component score (PCS) and mental health component score (MCS), with the change in MCS being statistically superior to the PCS (MCS, Hedges' g = 1.28; 95% confidence interval, 1.15-1.42; PCS, Hedges' g = 0.97; 95% confidence interval, 0.86-1.07). Medium, large, and very large effect sizes were observed for SF-36 subscales scores. Post-ECT depression remission status was related to HRQoL improvement, with statistically significant differences present between remitters and nonremitters for PCS, MCS, and most SF-36 subscale scores. No significant differences were observed in improvement in HRQoL with ECT based on patient age. CONCLUSIONS: An acute course of ECT for depressive symptoms produces medium to very large effect size improvements in HRQoL across multiple components and subscales measured by the SF-36. The magnitude of the effects reported by ECT patients is greater than those that have been reported in other open-label studies of brain stimulation techniques. This study confirms that ECT plays a vital role in the treatment of the most severely ill patients with depressive 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".