Current Practices of Electroconvulsive Therapy in Mental Disorders
Bibliographic record
Abstract
ABSTRACT: Electroconvulsive therapy (ECT) remains one of the most effective treatments for major depressive disorder, but uncertainties persist regarding the cognitive tests to include in ECT follow-up. The current study is a systematic review and meta-analysis of the most frequent cognitive side effects after ECT. We also discuss the most common cognitive tests in ECT follow-up. We searched studies published from 2000 to 2017 in English and French language in Pubmed, EBM Reviews, EMBASE, and PsycINFO. Standardized cognitive tests were separated into 11 cognitive domains. Comparisons between cognitive measures included pre-ECT baseline with post-ECT measures at 3 times: PO1, immediately post-ECT (within 24 hours after last ECT); PO2, short term (1-28 days); and PO3, long term (more than 1 month). A total of 91 studies were included, with an aggregated sample of 3762 individuals. We found no significant changes in global cognition with Mini-Mental State Examination at PO1. Hedges g revealed small to medium effect sizes at PO2, with individuals presenting a decrease in autobiographical memory, verbal fluency, and verbal memory. Verbal fluency problems showed an inverse correlation with age, with younger adults showing greater deficits. At PO3, there is an improvement on almost all cognitive domains, including verbal fluency and verbal memory. There is a lack of standardization in the choice of cognitive tests and optimal cognitive timing. The Mini-Mental State Examination is the most common screening test used in ECT, but its clinical utility is extremely limited to track post-ECT cognitive changes. Cognitive assessment for ECT purposes should include autobiographical memory, verbal fluency, and verbal memory.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".