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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it