Efficacy, Effectiveness, and Safety of Transcranial Magnetic Stimulation for Bipolar Depression: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Repetitive transcranial magnetic stimulation (rTMS) is cleared by the Food and Drug Administration for major depression, and recently received breakthrough status for bipolar depression (BDep). However, evidence on its efficacy and safety and optimal protocols for BDep remains limited. We conducted a systematic review to synthesize available data on rTMS for BDep. Methods: We systematically searched 4 literature databases for studies published between 1995 and 2025 treating participants with acute BDep (1097 articles). The primary outcome for the meta-analysis was change in mean depression severity scores from baseline. Determinants of treatment response were assessed using meta-regression and subgroup meta-analyses. Results: = 1.4), with rates of response (46.81%) and remission (28.25%) similar to those described for MDD and preserved in subanalyses for high-frequency protocols, including intermittent theta burst stimulation (iTBS) delivered to the left dorsolateral prefrontal cortex (DLPFC) and low-frequency protocols delivered to the right DLPFC. Higher baseline illness severity and more treatment sessions were predictors of greater antidepressant effect. Conclusions: TMS is efficacious and safe in BDep, with response and remission rates on par with rates for unipolar depression. High- and low-frequency protocols on the left and right DLPFC, respectively, are robustly associated with positive outcomes, with left DLPFC iTBS showing noninferiority to more widely used high-frequency rTMS protocols.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".