The Role of Transcranial Magnetic Stimulation in Treatment-Resistant Depression: A Review
Bibliographic record
Abstract
Major depressive disorder (MDD) is a prevalent mental illness associated with significant impairment in quality of life and treatment resistance in as many as 50% of patients. Few alternatives to psychopharmacological and electroconvulsive therapy (ECT) exist. Transcranial magnetic stimulation (TMS) is one such alternative with demonstrated efficacy in the treatment of both MDD and treatment- resistant depression (TRD). Accrued evidence from meta-analyses suggests that rTMS has moderate effect sizes in both MDD and TRD, comparable, though less robust, to those seen in ECT treated patients, and similar to those seen with antidepressant treatment in TRD. To date, rTMS has been used in adult, pediatric, and geriatric populations with success. Predictors of response include lower age, lower degrees of treatment resistance, and the absence of comorbid anxiety or psychotic symptoms. rTMS is cost-effective when compared to existing treatments for TRD including psychopharmacological interventions and ECT. More research, however, is needed to determine the most optimal stimulation parameters. Accelerated treatment over a short duration of time, sequential bilateral stimulation, extended number of pulses per session are potential methods of optimizing efficacy over current unilateral stimulation protocols. The extent to which rTMS can be pushed to engender the greatest possible clinical effects while avoiding seizure induction remains unknown.
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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 | 0.004 | 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".