Complementary and Integrative Medicine for the Treatment of Tourette's Syndrome
Bibliographic record
Abstract
BACKGROUND: There is widespread interest in complementary and integrative medicine (CIM) among people with Tourette's syndrome (TS). OBJECTIVE: To perform a systematic review of evidence on the use of CIM to reduce tics and improve tic-related quality of life. METHODS: We included clinical studies of CIM in children, adolescents and adults with TS and chronic tic disorders, and assessed the change in tic severity and/or tic-related quality of life using validated scales. Risk of bias of randomized controlled trials was assessed using the risk of bias tool of the American Academy of Neurology, which classifies studies into Class I, II, III or IV based on quality criteria. RESULTS: 49 clinical studies and three systematic reviews were included. Most studies were rated Class IV and therefore at high risk of bias. Class I studies demonstrated efficacy of functional MRI neurofeedback, 5-Ling granule, Jingxin Zhidong formula, and Ningdong granule in reducing tic severity. Class II studies suggest efficacy of mindfulness-based intervention for tics, acupuncture combined with atlantoaxial joint bone setting therapy, and art therapy. Systematic reviews summarizing the Chinese literature on acupuncture, acupuncture with herbal medicine and massage therapy suggest greater reduction in tics compared to conventional treatments but there is low confidence in the evidence due to poor methodological quality of included studies. CONCLUSIONS: Evidence to support the use of complementary and integrative medicine for TS is limited in methodological quality and widespread applicability. These limitations prohibit evidence-based recommendations for general use among individuals with TS.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".