Behavioural Therapy for tic disorders: a comprehensive review of the literature
Bibliographic record
Abstract
INTRODUCTION: Behavioral therapies are recommended as a first-line intervention for Tourette syndrome and persistent motor or phonic tic disorder. AREAS COVERED: In this review, the authors summarize randomized controlled trials on the comprehensive behavioral intervention for tics (CBIT), habit reversal therapy (HRT), and exposure and response prevention (ERP). Studies of face-to-face treatment, treatment by video conferencing, group treatment, and internet delivered treatment were assessed, as well as evidence of treatment predictors, modifiers, and mediators. EXPERT OPINION: There is high-quality evidence for face-to-face one-on-one treatment with CBIT, and data suggesting that one-on-one treatment by videoconference provides similar benefit. Limited data on group treatment with CBIT/HRT suggests inferiority to individual treatment, while internet-based CBIT programs appear more beneficial than wait list or psychoeducation. There is one face-to-face one-on-one treatment comparison of ERP to HRT, suggesting equal benefit. Internet-based ERP with minimal therapist support appears effective, although effect sizes are small. One study using behavioral therapy with ERP or HRT found similar benefit to medical treatment with antipsychotics. Data on predictors, modifiers, and mediators of treatment efficacy are emerging. In summary, behavioral therapies are an important treatment modality for tic disorders. Furthermore, important efforts to improve treatment accessibility are underway.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.001 |
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".