Deep brain stimulation for Gilles de la Tourette syndrome in children and youth: a meta-analysis with individual participant data
Bibliographic record
Abstract
Objective: Gilles de la Tourette syndrome (GTS) is a disorder characterized by motor and vocal tics. Although by definition the onset of GTS is before age 18 years, clinical trials of deep brain stimulation (DBS) have been conducted only in adults. Using individual participant data (IPD) meta-analysis methodology, the current study investigated the safety and efficacy of DBS as a treatment for GTS in children and youth. Methods: A systematic review with no date or language restrictions was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. Three electronic databases were searched: PubMed, EMBASE, and Web of Science. From 843 articles screened, the IPD of 58 children and youth (ages 12–21 years) extracted from 21 articles were collected and analyzed. A mixed-effects univariable analysis followed by multivariable hierarchical regression was performed using change in the Yale Global Tic Severity Scale (YGTSS) score as the primary outcome and reported measures of comorbidities as secondary outcomes. Results: The authors’ results showed an average improvement of 57.5% ± 24.6% across studies on the YGTSS. They also found that comorbid depression and stimulation pulse width each correlated negatively with outcome (p < 0.05). In patients with less severe GTS, greater improvements were evident following thalamic stimulation. More than one-quarter (n = 16, 27.6%) of participants experienced side effects, the majority of which were minor. Conclusions: DBS in the pediatric population may be an effective option with a moderate safety profile for treatment of GTS in carefully selected children and youth. Large, prospective studies with long-term follow-up are necessary to understand how DBS influences tic symptoms and may alter the natural course of GTS in 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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".