A randomized double-blind sham-controlled comparison of unilateral and bilateral repetitive transcranial magnetic stimulation for treatment-resistant major depression
Bibliographic record
Abstract
OBJECTIVES: High frequency left-sided (HFL) and low frequency right-sided (LFR) unilateral repetitive transcranial magnetic stimulation (rTMS) are efficacious in treatment-resistant major depression (TRD). Similar benefit has been suggested for sequential bilateral rTMS (LFR then HFL). There are few published reports on the efficacy of sequential bilateral rTMS compared to HFL and sham rTMS. Therefore, this study evaluated the efficacy of HFL and sequential bilateral rTMS compared to sham in TRD. METHODS: Subjects between the ages of 18 and 85 were recruited from a tertiary care university hospital. Seventy-four subjects with TRD and a 17-item Hamilton Depression Rating Scale (HDRS) greater than 21 were randomized to receive unilateral, bilateral, or sham rTMS. The rates of remission were compared among the three treatment groups. RESULTS: The remission rates differed significantly among the three treatment groups using a modified intention to treat analysis that excluded subjects who did not respond to electroconvulsive therapy (ECT) during the current episode. The remission rate was significantly higher in the bilateral group than the sham group. The remission rate in the unilateral group did not differ from either group. CONCLUSION: These findings warrant larger controlled studies that compare the efficacy of sequential bilateral rTMS and HFL rTMS in TRD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".