Transcranial magnetic stimulation for posttraumatic stress disorder: an updated systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Transcranial magnetic stimulation (TMS) is a promising non-pharmacological intervention for posttraumatic stress disorder (PTSD). However, randomized controlled trials (RCTs) and meta-analyses have reported mixed results. OBJECTIVE: To review articles that assess the efficacy of TMS in PTSD treatment. METHODS: A systematic review using MEDLINE and other databases to identify studies from the first RCT available up to September 2015. The primary outcome was based on PTSD scores (continuous variable). The main outcome was Hedges' g. We used a random-effects model using the statistical packages for meta-analysis available in Stata 13 for Mac OSX. Heterogeneity was evaluated with I2 (> 35% for heterogeneity) and the χ2 test (p < 0.10 for heterogeneity). Publication bias was evaluated using a funnel plot. Meta-regression was performed using the random-effects model. RESULTS: Five RCTs (n = 118) were included. Active TMS was significantly superior to sham TMS for PTSD symptoms (Hedges' g = 0.74; 95% confidence interval = 0.06-1.42). Heterogeneity was significant in our analysis (I2 = 71.4% and p = 0.01 for the χ2 test). The funnel plot shows that studies were evenly distributed, with just one study located marginally at the edge of the funnel and one study located out of the funnel. We found that exclusion of either study did not have a significant impact on the results. Meta-regression found no particular influence of any variable on the results. CONCLUSION: Active TMS was superior to sham stimulation for amelioration of PTSD symptoms. Further RCTs with larger sample sizes are fundamental to clarify the precise impact of TMS in PTSD.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".