Effect of repetitive transcranial magnetic stimulation on anxiety symptoms in patients with major depression: An analysis from the THREE‐D trial
Bibliographic record
Abstract
BACKGROUND: Despite the advances in the use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD), there is relatively little information about its effect on comorbid anxiety symptoms. METHODS: Data from a large randomized noninferiority trial comparing intermittent theta-burst stimulation (iTBS) and high-frequency (10 Hz) rTMS delivered to the left dorsolateral prefrontal cortex (HFL) were analyzed. The primary aim was assessing changes in anxiety/somatization items from the 17-item Hamilton Depression Rating Scale (HAM-D) and the Brief Symptom Inventory (BSI-A), using baseline-adjusted change with an analysis of covariance (ANCOVA), with the final scores as the outcome and baseline scores as the adjustment covariates. RESULTS: The analytical cohort comprised 388 participants (189 in HFL and 199 in iTBS groups). From baseline to the end of the rTMS course, the combined score from the anxiety items from the HAM-D dropped from 7.43 (SD = 2.15) to 4.24 (SD = 2.33) in the HFL group, and 7.33 (SD = 2.13) to 3.76 (SD = 2.23) in the iTBS group. The ANCOVA resulted in an effect from time (p < .0001), but not from group allocation (p = .793) or time × group interaction (p = .976). We observed mean changes in the BSI-A of -3.5 (SD = 5.4) and -3.2 (SD = 4.8), with significant effect of time (p < .0001) in the ANCOVA, but not group allocation (p = .793) or group × time interaction (.664). CONCLUSIONS: Our findings suggest that both 10 Hz and iTBS may yield potential reductions in anxiety symptoms when used for the treatment of MDD. Our findings warrant future research into the effects of left-sided rTMS on depressed patients struggling with concurrent anxiety symptoms.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".