Transcranial Magnetic Stimulation in the Treatment of Mood Disorder: A Review and Comparison with Electroconvulsive Therapy
Bibliographic record
Abstract
OBJECTIVE: To review repetitive transcranial magnetic stimulation (rTMS) as a mode of therapy for depression. METHOD: The following aspects of rTMS were reviewed and compared with electroconvulsive therapy (ECT): history, basic principles, technical considerations, possible mode of action, safety, adverse effects, and effects on mood in both healthy individuals and those suffering from bipolar disorder (BD) or depression. RESULTS: rTMS may selectively increase or decrease neuronal activity over discrete brain regions. As a result of this focused intervention with TMS, the potential for unwanted side effects is substantially reduced, compared with ECT. In open trials, rTMS and ECT are reported to be equally efficacious for patients having depression without psychosis, but the therapeutic benefits reported in double-blind sham-rTMS controlled trials are more modest. CONCLUSION: The antidepressant and antimanic effects of rTMS depend on technical considerations such as stimulus frequency, intensity, and magnetic coil placement, which may not yet be optimized. Biological heterogeneity among the patients treated with rTMS may also contribute to differing efficacy across clinical trials. rTMS may possess tremendous potential as a treatment for mood disorder, but this has not yet been realized. rTMS must still be regarded as an experimental intervention requiring further refinement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".