Bilateral transcranial magnetic stimulation of the supplementary motor area in children with Tourette syndrome
Bibliographic record
Abstract
AIM: To explore the feasibility and possible effects of low-frequency repetitive transcranial magnetic stimulation (rTMS) delivered to the supplementary motor area (SMA) on tic severity and motor system neurophysiology in children with Tourette syndrome. METHOD: Ten children with Tourette syndrome (eight males, two females; 9-15y) participated in this open-label, phase 1 clinical trial. Treatment consisted of 1800 low-frequency (1Hz) neuronavigated robotic rTMS (100% resting motor threshold) to the SMA, bilaterally for 15 sessions. The primary outcome was a change in Yale Global Tic Severity Scale (YGTSS) total score from baseline to posttreatment. Secondary outcome measures included changes in magnetic resonance spectroscopy metabolite concentrations, TMS neurophysiology measures, TMS motor maps, and clinical assessments (anxiety, depression) from baseline to the end of treatment. RESULTS: The YGTSS score decreased from baseline after treatment (p<0.001; Cohen's d=2.9). All procedures were well-tolerated. INTERPRETATION: Robot-driven, neuronavigated bilateral rTMS of the SMA is feasible in children with Tourette syndrome and appears to reduce tic severity. What this paper adds Repetitive transcranial magnetic stimulation (rTMS) is feasible to use in children with Tourette syndrome. rTMS is tolerated by children with Tourette syndrome. Precise targeting of the supplementary motor area using functional magnetic resonance imaging is also feasible in these children.
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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".