Augmenting antidepressants with deep transcranial magnetic stimulation (DTMS) in treatment-resistant major depression
Bibliographic record
Abstract
OBJECTIVES: Deep transcranial magnetic stimulation (DTMS) has been shown to be efficacious and relatively safe for major depressive disorder (MDD). However, its clinical utility as an augmenting strategy for treatment-resistant depression (TRD) remains unexplored. METHODS: In an open label trial, 17 outpatients with severe TRD received 4 weeks of daily high frequency DTMS over the left dorsolateral prefrontal cortex. Depressive and anxious symptoms, suicidality and quality of life (QOL) were measured at baseline (i.e., in the week prior to the start of the DTMS treatment) and at week 5 (i.e., in the week following the end of the DTMS treatment). Primary outcome measures were rates of response and remission at week 5 using an intention-to-treat approach. RESULTS: Response and remission rates at week 5 were 70.6 and 41.2%, respectively. Also, depression, anxiety, and suicidality ratings were significantly improved by week 5 (with Hedges' g estimates ranging from 0.6 to 1.72), as well as four of the five QOL domain scores (i.e., global, psychological, environmental and social). Finally, two patients dropped out of the study at week 1 because of significant scalp discomfort during stimulation. CONCLUSIONS: Our study suggests that DTMS, when used as an augmenting strategy for antidepressants in severe TRD, is efficacious, safe and relatively well tolerated. However, controlled studies with larger samples are needed to confirm and expand our preliminary findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".