Efficacy of Repetitive Transcranial Magnetic Stimulation on Comorbid Anxiety and Depression Symptoms in Obsessive-Compulsive Disorder: A Meta-Analysis of Randomized Sham-Controlled Trials
Bibliographic record
Abstract
Objective To systematically evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) in reducing comorbid anxiety and depressive symptoms in patients with obsessive-compulsive disorder (OCD). Methods Three electronic databases were searched for randomized, sham-controlled clinical trials evaluating rTMS for the treatment of OCD. Hedge's g was calculated as the effect size for anxiety/depression symptom severity (primary outcome) and OCD severity (secondary outcome). Subgroup analyses and meta-regression analyses were carried out to evaluate the most promising target and whether a reduction in OCD severity moderates the change in anxiety or depression scores. Results Twenty studies ( n = 688) were included in the meta-analysis. rTMS had small-medium effect size on OCD (Hedge's g = 0.43; 95% confidence interval [CI]: [0.20, 0.65]; P < 0.001), anxiety (Hedge's g = 0.3; 95% CI: [0.11, 0.48]; P = 0.001) and depression (Hedge's g = 0.24; 95% CI: [0.07, 0.40]; P = 0.003) symptoms. Subgroup analysis showed that protocols targeting dorsolateral prefrontal cortex (DLPFC) were effective for 3 outcome measures. The change in anxiety, but not depression severity, was moderated by a change in OCD symptom scores. However, the findings are uncertain as a majority of the studies had some concerns or a high risk of bias. Conclusions Active rTMS protocol targeting DLPFC is effective in reducing the comorbid anxiety/depression symptoms along with OCD severity. The antidepressant effect is not moderated by the anti-obsessive effect of rTMS.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".