Appraising the effectiveness of electrical and magnetic brain stimulation techniques in acute major depressive episodes: an umbrella review of meta-analyses of randomized controlled trials
Bibliographic record
Abstract
Electrical and magnetic brain stimulation techniques present distinct mechanisms and efficacy in the acute treatment of depression. This was an umbrella review of meta-analyses of randomized controlled trials of brain stimulation techniques for managing acute major depressive episodes. A systematic review was performed in the PubMed/MEDLINE databases from inception until March 2020. We included the English language meta-analysis with the most randomized controlled trials on the effects of any brain stimulation technique vs. control in adults with an acute depressive episode. Continuous and dichotomous outcomes were assessed. A Measurement Tool to Assess Systematic Reviews-2 was applied and the credibility of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation framework. Seven meta-analyses were included (5,615 patients), providing evidence for different modalities of brain stimulation techniques. Three meta-analyses were evaluated as having high methodological quality, three as moderate, and one as low. The highest quality of evidence was found for high frequency-repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation, and bilateral rTMS. There is strong clinical research evidence to guide future clinical use of some techniques. Our results confirm the heterogeneity of the effects across these techniques, indicating that different mechanisms of action lead to different efficacy profiles.
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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 | 0.044 | 0.122 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".