Effectiveness of Repetitive Transcranial Magnetic Stimulation (rTMS) Add-On Therapy to a Standard Treatment in Individuals with Multiple Sclerosis and Concomitant Symptoms of Depression—Results from a Randomized Clinical Trial and Pilot Study
Bibliographic record
Abstract
BACKGROUND: Compared to the general population, persons with multiple sclerosis (MS) are at increased risk of suffering from major depressive disorder (MDD). Repetitive Transcranial Magnetic Stimulation (rTMS) was used successfully to treat individuals with MDD. Here, we conducted a randomized clinical trial and pilot study, and tested the effectiveness of rTMS adjuvant to a standard pharmacological treatment among persons with MS, compared to a sham condition. MATERIALS AND METHODS: A total of 40 persons with MS (mean age: 32 years; 42.5% females; median EDSS score: 4) and with moderate to severe symptoms of depression were randomly assigned to the rTMS or to the rTMS sham condition, always as adjuvant intervention to the standard treatment with sertraline, a selective serotonin reuptake inhibitor (SSRI). rTMS consisted of 10 sessions each of 37.5 min; the sham condition was identical to the active condition except for the absence of rTMS stimuli. At the beginning and two weeks after the end of the study, participants reported on their fatigue, while experts rated the severity of participants' depressive symptoms (Montgomery-Asberg Depression Rating Scale; MADRS), cognitive performance (Montreal Cognitive Assessment; MoCA), and degree of disability (Expanded Disability Status Scale; EDSS). RESULTS: Data were analyzed per intent-to-treat. Scores for depression, fatigue, and EDSS declined significantly over time (large effect sizes), but more so in the rTMS condition than in the sham condition (large effect sizes for the time by group-interactions). Compared to the sham condition, scores for depression were significantly lower in the rTMS condition. Scores for cognition improved over time in both study conditions (large effect size). CONCLUSION: Compared to a sham condition, adjuvant rTMS to a standard pharmacological treatment ameliorated typical MS-related symptoms (depression; fatigue; EDSS scores). Results from this pilot study suggested that rTMS might be routinely applied in persons with MS displaying symptoms of depression and fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".