Deep Brain Stimulation Is Beneficial for Treatment-Resistant Depression: A Systematic Review and Meta-Analysis of Clinical Trials and Long-Term Follow-Ups
Bibliographic record
Abstract
BACKGROUND: Most conventional treatments for MDD (major depressive disorder) fail to demonstrate significant results, which highlights an urgent need for novel treatments. DBS (deep brain stimulation) has shown great promise in several clinical trials. However, the efficacy of DBS, compared to conventional treatments for patients with TRD (treatment-resistant depression), and the targets with highest efficiency for treatment of TRD remain understudied objectives. METHODS: A systematic search in PubMed, ProQuest Dissertations and Theses Global, Embase, PsycINFO, and Scopus accompanied by a hand search in journals and grey literature was conducted in (January 10th, 2025). Any controlled trial with at least one group for comparison such as sham group was included. A random-effects meta-analysis was performed, quantitatively probing the efficacy of DBS vs sham/placebo for patients with TRD. A meta-analysis of response rate after long-term follow-up was also conducted. In addition, a meta-regression analysis was implemented to detect potentially moderating variables. Quality assessment was performed by utilizing the NOS (Newcastle Ottawa Scale) and Jadad scale. RESULTS: 1744 records were identified and screened for relevance, among which 14 were included in the quantitative analysis. Meta-analysis of the included studies revealed a medium-large effect size (SMD (standardized mean difference) Hedge’s g = -0.51) in favor of active stimulation compared to sham. In the sensitivity analysis the effect size was statistically insignificant only when randomized controlled trials were included. The effect size and pooled response rate after long-term follow-up was = -1.12 and 56.14%, respectively. CONCLUSION: The results indicate that DBS is potentially favorable for TRD compared to sham, as DBS exhibited greater efficacy compared to sham in both clinical trial and long-term follow-up phases. Further large-scale evidence-based studies, however, are required to substantially support these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".