Response, remission and drop-out rates following high-frequency repetitive transcranial magnetic stimulation (rTMS) for treating major depression: a systematic review and meta-analysis of randomized, double-blind and sham-controlled trials
Bibliographic record
Abstract
BACKGROUND: Meta-analyses have shown that high-frequency (HF) repetitive transcranial magnetic stimulation (rTMS) has antidepressant properties when compared with sham rTMS. However, its overall response and remission rates in major depression (MD) remain unclear. Thus, we have systematically and quantitatively assessed the efficacy of HF-rTMS for MD based on randomized, double-blind and sham-controlled trials (RCTs). METHOD: We searched the literature from 1995 through to July 2012 using MEDLINE, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, SCOPUS, and ProQuest Dissertations & Theses. We used a random-effects model, odds ratios (ORs) and the number needed to treat (NNT). RESULTS: Data from 29 RCTs were included, totaling 1371 subjects with MD. Following approximately 13 sessions, 29.3% and 18.6% of subjects receiving HF-rTMS were classified as responders and remitters, respectively (compared with 10.4% and 5% of those receiving sham rTMS). The pooled OR was 3.3 (p < 0.0001) for both response and remission rates (with associated NNTs of 6 and 8, respectively). Furthermore, we found HF-rTMS to be equally effective as an augmentation strategy or as a monotherapy for MD, and when used in samples with primary unipolar MD or in mixed samples with unipolar and bipolar MD. Also, alternative stimulation parameters were not associated with differential efficacy estimates. Moreover, baseline depression severity and drop-out rates at study end were comparable between the HF-rTMS and sham rTMS groups. Finally, heterogeneity between the included RCTs was not statistically significant. CONCLUSIONS: HF-rTMS seems to be associated with clinically relevant antidepressant effects and with a benign tolerability profile.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".