Repetitive transcranial magnetic stimulation (rTMS) for treating major depression and schizophrenia: a systematic review of recent meta-analyses
Bibliographic record
Abstract
BACKGROUND: In recent years, repetitive transcranial magnetic stimulation (rTMS) has been developed for the treatment of major depression (MD) and schizophrenia. Although rTMS has shown some promising findings, the lack of standardization in the methodology employed has resulted in discordant findings. OBJECTIVES: The objective of this systematic review was to summarize several meta-analytical studies exploring the efficacy of rTMS in either MD or schizophrenia in order to examine the methodologies that increase the efficacy of rTMS and to provide some recommendations for future studies. METHODS: We searched the MEDLINE database for potentially relevant meta-analytic studies on the use of rTMS for treating major depression and schizophrenia published from January 2000 to October 2011. RESULTS: Fifteen rTMS meta-analytical studies were reviewed (11 on MD and 5 on schizophrenia). Several variables were reviewed including outcome measures, side-effects of rTMS, site of stimulation, frequency and intensity of stimulation, and number of treatment sessions. CONCLUSIONS: Overall, rTMS appears to be an effective and promising therapeutic for both MD and schizophrenia.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".