The use of repetitive transcranial magnetic stimulation and vagal nerve stimulation in the treatment of depression
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with depressive disorders often fail to respond to standard antidepressant medications and have few available treatment alternatives. Repetitive transcranial magnetic stimulation and vagal nerve stimulation have been developed and investigated over the last 10 years as potential treatment options for this and other psychiatric conditions. The aim of this paper is to review recent therapeutic trials of these techniques. RECENT FINDINGS: Recent studies appear to have confirmed that standard left-sided repetitive transcranial magnetic stimulation has antidepressant efficacy, but that the degree of clinical effect may be somewhat limited. Promising data are emerging suggesting that other approaches, including right unilateral repetitive transcranial magnetic stimulation and sequential bilateral stimulation, may have equal or potentially greater effects. The evidence for the effectiveness of vagal nerve stimulation remains restricted to the primary company-sponsored trials. Although limited, these data suggest that valuable treatment effects may develop over time. SUMMARY: Further repetitive transcranial magnetic stimulation research should actively investigate novel stimulation approaches before high-frequency left-sided stimulation is accepted as the standard approach. Given the invasive nature of vagal nerve stimulation and potential side effects, further research is urgently required. This should include the development of predictors of clinical response and definition of stimulation parameters with enhanced efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.002 |
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".