A randomized trial of low-frequency right-prefrontal-cortex transcranial magnetic stimulation as augmentation in treatment-resistant major depression
Bibliographic record
Abstract
Low-frequency right prefrontal repetitive transcranial magnetic stimulation (rTMS) appears to have antidepressant properties although the effectiveness of this treatment in clinical practice has not been assessed nor have the optimal stimulation parameters been adequately defined. A total of 130 patients with treatment-resistant depression were randomized to either 1- or 2-Hz rTMS over the right prefrontal cortex (PFC) for 2 wk with a possible further 2 wk extension. Non-responders were randomized to either 5- or 10-Hz left PFC rTMS. Overall, 66 patients (51%) achieved response and 35 (27%) remission criteria. For right-sided treatment, depression significantly improved but there was no between-group difference. Twenty-eight (42%) patients in the 1-Hz group and 33 (53%) patients in the 2-Hz group achieved response criteria (chi2=1.40, p>0.05). Depression symptom scores also improved for patients who crossed over to left-sided treatment but there was no significant difference in response between 5- and 10-Hz rTMS. Despite a heterogeneous sample, a significant proportion of patients met clinical response criteria following treatment but response to 1 and 2 Hz did not differ. 2-Hz right PFC rTMS has antidepressant properties but offers no advantage over 1 Hz despite doubling pulse number.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".