EFFICACY AND ACCEPTABILITY OF HIGH FREQUENCY REPETITIVE TRANSCRANIAL MAGNETIC STIMULATION (rTMS) VERSUS ELECTROCONVULSIVE THERAPY (ECT) FOR MAJOR DEPRESSION: A SYSTEMATIC REVIEW AND META-ANALYSIS OF RANDOMIZED TRIALS
Bibliographic record
Abstract
Clinical trials comparing the efficacy and acceptability of high frequency repetitive transcranial magnetic stimulation (HF-rTMS) and electroconvulsive therapy (ECT) for treating major depression (MD) have yielded conflicting results. As this may have been the result of limited statistical power, we have carried out this meta-analysis to examine this issue. We searched the literature for randomized trials on head-to-head comparisons between HF-rTMS and ECT from January 1995 through September 2012 using MEDLINE, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, and SCOPUS. The main outcome measures were remission rates, pre-post changes in depression ratings, as well as overall dropout rates at study end. We used a random-effects model, Odds Ratios (OR), Number Needed to Treat (NNT), and Hedges' g effect sizes. Data were obtained from 7 randomized trials, totalling 294 subjects with MD. After an average of 15.2 HF-rTMS and 8.2 ECT sessions, 33.6% (38/113) and 52% (53/102) of subjects were classified as remitters (OR = 0.46; p = 0.04), respectively. The associated NNT for remission was 6 and favoured ECT. Also, reduction of depressive symptomatology was significantly more pronounced in the ECT group (Hedges' g = -0.93; p = 0.007). No differences on dropout rates for HF-rTMS and ECT groups were found. In conclusion, ECT seems to be more effective than HF-rTMS for treating MD, although they did not differ in terms of dropout rates. Nevertheless, future comparative trials with larger sample sizes and better matching at baseline, longer follow-ups and more intense stimulation protocols are warranted.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".